Environmental factors controlling soil organic carbon stability in French forest soils Laure Soucémarianadin, Lauric Cécillon, Bertrand Guenet, Claire Chenu, François Baudin, Manuel Nicolas, Cyril Girardin, Pierre Barré
To cite this version: Laure Soucémarianadin, Lauric Cécillon, Bertrand Guenet, Claire Chenu, François Baudin, et al.. Environmental factors controlling soil organic carbon stability in French forest soils. Plant and Soil, Springer Verlag, In press, .
HAL Id: hal-01744147 https://hal.archives-ouvertes.fr/hal-01744147 Submitted on 27 Mar 2018
HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.
Revised version without track changes
Click here to download Revised version without track changes renamed_a9f36.docx
1
Environmental factors controlling soil organic carbon stability in French forest
2
soils
3 4 5
Laure N. Soucémarianadin1,*, Lauric Cécillon2, Bertrand Guenet3, Claire Chenu4, François
6
Baudin5, Manuel Nicolas6, Cyril Girardin4 and Pierre Barré1
7 8
1
Laboratoire de Géologie, PSL Research University, CNRS-ENS UMR8538, Paris, France
9
2
Université Grenoble Alpes, Irstea, UR LESSEM, St-Martin-d'Hères, France
10
3
Laboratoire des Sciences du Climat et de l’Environnement, LSCE/IPSL, CEA-CNRS-
11
UVSQ, Université Paris-Saclay, Gif-sur-Yvette, France
12
4
AgroParisTech-INRA, UMR ECOSYS, Thiverval-Grignon, France
13
5
Sorbonne-Université/UPMC, ISTeP, Paris, France
14
6
Office National des Forêts, R&D, Fontainebleau, France
15 16
* Corresponding author: Laure Soucémarianadin,
[email protected]
17
Laboratoire de Géologie (UMR 8538) Ecole Normale Supérieure, 24 Rue Lhomond 75231
18
Paris CEDEX 5, France; phone: +331 44 32 22 94; fax: +331 44 32 22 00
19 20 21 22 23
Type: Regular article
1
24
Abstract
25
Aims
26
In temperate forests, soils contain a large part of the ecosystem carbon that can be partially
27
lost or gained upon global change. Our aim was to identify the factors controlling soil organic
28
carbon (SOC) stability in a wide part of French forests.
29 30
Methods
31
Using a set of soils from 53 French forest sites, we assessed the effects of depth (up to 1 m),
32
soil class (dystric Cambisol; eutric Cambisol; entic Podzol), vegetation types (deciduous;
33
coniferous) and climate (continental influence; oceanic influence; mountainous influence) on
34
SOC stability using indicators derived from laboratory incubation, physical fractionation and
35
thermal analysis.
36 37
Results
38
Labile SOC pools decreased while stable SOC pool increased with depth. Soil class also
39
significantly influenced SOC stability. Eutric Cambisols had less labile SOC in surface layers
40
but had more labile SOC at depth (> 40 cm) than the other soil classes. Vegetation influenced
41
thermal indicators of SOC pools mainly in topsoils (0–10 cm). Mountainous climate forest
42
soils had a low thermal SOC stability.
43 44
Conclusions
45
On top of the expected effect of depth, this study also illustrates the noticeable effect of soil
46
class on SOC stability. It suggests that environmental variables should be included when
47
mapping climate regulation soil service.
48 2
49 50
Keywords: forest soils, particulate organic matter fractionation, pedology, Rock-Eval 6, soil
51
basal respiration, soil organic carbon persistence
52 53
Abbreviations: soil organic carbon (SOC), Rock-Eval 6 (RE6), particulate organic matter
54
(POM), Oxygen index (OIRE6), Hydrogen index (HI), Hydrocarbons (HC)
55 56
57
Introduction
58
Forest ecosystems play a central role in the global carbon (C) cycle with their high potential
59
for atmospheric CO2 sequestration (Intergovernmental Panel on Climate Change 2000; Smith
60
et al. 2014). About half of the terrestrial C sink is indeed located in forests (Canadell et al.
61
2007) and forest soils in particular store around 398 Pg C (Kindermann et al. 2008). The
62
temperate biome holds a quarter of the world’s forests (Tyrrell et al. 2012) and soils in
63
temperate forests have twice as much carbon as the vegetation (Jarvis et al. 2005). Temperate
64
forest soils have also constituted a C sink for over two decades (Pan et al. 2011; Tyrrell et al.
65
2012) with parts of the European—and particularly French (Jonard et al. 2017)—forest soils
66
being particularly efficient at sequestering C (e.g., Nabuurs et al., 2008). The contribution of
67
deep soil to soil organic carbon (SOC) stocks has been previously highlighted (Jobbágy and
68
Jackson 2000; Rumpel and Kögel-Knabner 2010), however there is still a lack of data on
69
deep/subsoil mineral (> 30 cm depth) SOC stocks (e.g., Tyrrell et al. 2012).
70
SOC is made up of very heterogeneous compounds (Amundson 2001; Trumbore 1997) with
71
turnover times ranging from a few days/weeks to several centuries and, for modelling
3
72
purposes, can be roughly divided into active (labile), intermediate and passive (persistent)
73
SOC kinetic pools.
74
Labile SOC is the most likely to be quickly affected by climate or land-use changes (Carter et
75
al. 1998; Zhang et al. 2007), thus potentially contributing further to global warming.
76
Moreover, because of the central role of the SOC labile pool in short-to medium-term nutrient
77
availability and soil structural stability (Wander 2004), its evolutions could have major effects
78
on biomass (food/timber/etc.) production. Conversely when considering SOC long-term
79
storage and possible sequestration, soils in which most of the total SOC is stable will perform
80
better than soils with a greater proportion of their total SOC as labile SOC (Jandl et al. 2007;
81
Lorenz and Lal 2010; Prescott 2010). It is thus essential to determine how much labile and
82
persistent SOC are present in soils.
83
Despite being of such interest, there is still no standard technique to assess SOC stability but a
84
set of complementary techniques are available. Respiration measurements and particulate
85
organic matter (POM) quantification obtained by various methods of fractionation (density
86
only / density + particle-size) (von Lützow et al. 2007) have been used for decades and are
87
traditional metrics of SOC lability. Although the respired-C and POM-C fractions both
88
represent a labile SOC pool, the former corresponds to a smaller SOC pool with a shorter
89
mean residence time (usually < 1 year for temperate in-situ conditions) (Feng et al. 2016)
90
while the latter corresponds to a larger SOC pool with a longer mean residence time (usually
91
< 20 year for temperate conditions (e.g., Balesdent 1996; Trumbore et al. 1996). This longer
92
residence time may result from interactions with the soil structure; part of the POM-C fraction
93
being occluded in micro-aggregates and protected from microbial respiration for longer time
94
scales (Six et al. 2002). The mean residence time of the POM-C fraction can also exceed 20
95
years, especially in cold and mountainous areas (Leifeld et al. 2009) or in areas affected by
96
wildfires where the POM-C fraction may contain large amounts of pyrogenic carbon with
4
97
residence time in soils greater than the mean residence time of total SOC. Nevertheless, it has
98
been shown that the POM-C fraction of temperate and mountainous soils of agroecosystems
99
correspond roughly to the resistant material pools (RPM) of the RothC model (Zimmerman et
100
al. 2007), which has a turnover rate of 3 years (Coleman and Jenkinson 1999). In this paper,
101
the respired-C fraction will be referred to as the highly-labile SOC pool and the POM-C
102
fraction will be termed labile SOC pool.
103
Thermal analysis techniques have also been used to characterize soil organic matter (SOM)
104
stability (e.g., Plante et al. 2009). Among them, Rock-Eval 6 (RE6) analysis has shown
105
promising results in the determination of SOM biogeochemical stability (Barré et al. 2016;
106
Gregorich et al. 2015; Saenger et al. 2015; Sebag et al. 2016). RE6-derived parameters are
107
reliable indicators of the stable SOC pool (Barré et al. 2016; Cécillon et al. 2018) and can be a
108
useful complement to the aforementioned usual indicators of the labile SOC pool
109
(Soucémarianadin et al. 2018). Specifically, one RE6-derived parameter, T50_HC_PYR, which
110
corresponds to the temperature at which 50% of the hydrocarbons released as pyrolysis
111
effluents during SOM pyrolysis have evolved, was linked to the highly-labile and the labile
112
SOC pools (Gregorich et al. 2015; Soucémarianadin et al., 2018). In French forest soils,
113
T50_HC_PYR was shown to be strongly and negatively correlated to the POM-C fraction but not
114
to the respired-C fraction (Soucémarianadin et al. 2018). T50_HC_PYR could thus be used as an
115
indicator of the labile SOC pool defined above, similarly to the POM-C fraction. Another
116
RE6-derived parameter, the temperature at which 50% of the CO2 resulting from SOM
117
thermal oxidation has evolved (T50_CO2_OX) was positively related to an increasing proportion
118
of persistent SOC (Barré et al. 2016; Cécillon et al. 2018) and to a POM-C depletion in
119
temperate forest soils (Soucémarianadin et al. 2018). T50_CO2_OX could thus be used as an
120
indicator of the stable SOC pool with mean residence times greater than several decades (>
121
50–100 years; Cécillon et al. 2018).
5
122
Few studies have assessed the factors controlling SOC stability over large areas. Several
123
recent studies have highlighted the importance of parent material and soil type on SOC
124
content and stability, SOC in deep soil layers being generally more stable compared to surface
125
SOC (Barré et al. 2017; Mason et al. 2016; Mathieu et al. 2015; Mulder et al. 2015). Camino-
126
Serrano et al. (2014) reported a larger highly labile SOC pool (based on concentrations of
127
dissolved organic carbon; DOC) in soils types characterized by a very acidic pH than in more
128
basic soils, especially in the subsoil layers below 20 cm depth. Considering croplands and
129
grasslands in Germany, Vos et al. (2017) showed that sandy soils had a larger labile SOC pool
130
(POM-C fraction) than soils with finer texture.
131
Land cover and vegetation type have also been shown to strongly control SOC stability.
132
Wiesmeier et al. (2014) found lower proportions of stable SOC pool in Bavarian forests
133
compared to grasslands or croplands, confirming results across Europe that showed that
134
afforestation of cropland and grassland generally decreased SOC stability (Poeplau and Don
135
2013). In the temperate forests of Bavaria, vegetation type was also shown to control SOC
136
stability, coniferous forests having higher labile SOC proportions than deciduous and mixed
137
forests (Wiesmeier et al. 2014). Similar results were obtained for the highly labile SOC pool
138
with lower dissolved organic carbon concentrations in broadleaved forests than coniferous
139
forests (Camino-Serrano et al. 2014). Variations of soil respiration were also observed at the
140
species level (e.g., three species of oaks; You et al. 2016).
141
Regarding climate, both global and more local studies have highlighted the strong positive
142
relationship with precipitation and the negative effect of temperature on SOC quantity
143
(Jobbágy and Jackson, 2000; Paul et al., 2002; Callesen et al., 2003; Wiesmeier et al. 2013).
144
Labile OM and particularly the POM-C fraction, has been shown to dominate in soils located
145
at higher elevations and experiencing colder mean annual temperatures (e.g., Leifeld et al.
146
2009; Sjögersten et al. 2011). Considering over 300 forested sites, higher DOC concentrations
6
147
(highly labile SOC pool) were found in temperate sites than boreal and tropical sites (Camino-
148
Serrano et al. 2014). To the exception of the work of Wiesmeier et al. (2014), we are not
149
aware of large scale studies that would consider both the highly labile, labile and stable SOC
150
pools and devoted to forest soils, despite their large SOC stocks.
151
The objectives of the study were thus to assess the importance of various environmental
152
factors, soil depth, soil class, vegetation type and climate class in controlling the stability of
153
SOC in forest soils. To this purpose, we used a set of complementary techniques, namely the
154
Rock-Eval 6 thermal analysis, POM separation by size and density and a laboratory 10-week
155
incubation, and applied them to a large set of French forest soil samples that covers a large
156
pedoclimatic variability, a wide tree species diversity and includes deep samples (up to 1
157
meter).
158
We hypothesized that 1/ SOC stability would vary with depth with surface soil layers
159
containing more labile SOC while deep soil layers would contain more stable SOC; 2/
160
vegetation type would influence SOC stability mostly in surface soil layers (with higher rates
161
of C input from plants), SOC being more labile in topsoils of coniferous forests; 3/ soil class
162
would influence SOC stability mostly in medium/deep soil layers (below 20 cm); and 4/
163
climate would influence SOC stability and SOC in mountainous plots would be more labile.
164
165
Material and methods
166
a. Sites and soil samples
167
We considered forest mineral soil samples from 53 permanent forest sites of the French
168
national network for the long term monitoring of forest ecosystems (‘‘RENECOFOR’’),
169
established in 1992 (Ulrich 1995) by the National Forest Service (ONF;
170
http://www.onf.fr/renecofor) as a part of the European ICP-FORESTS (http://icp-forests.net/)
171
level 2 network (Fig. 1a). Our selected sites are variable in terms of climate (continental 7
172
influence; oceanic influence; mountainous influence; with mean annual precipitation MAP
173
and mean annual temperature MAT ranging between 703–1894 mm and 4.8–12.3 °C
174
respectively for the 1971–2000 period), soil type with a class constituted of soils related to
175
entic Podzols, another class constituted of eutric and epileptic Cambisols as well as Calcisols
176
and a last class constituted of dystric and hyperdystric Cambisols (IUSS Working Group
177
2015) and forest vegetation with coniferous [silver fir (Abies alba Mill.); Norway spruce
178
(Picea abies (L.) H. Karst.); European larch (Larix decidua Mill.); Scots pine (Pinus
179
sylvestris L.)] and deciduous [beech (Fagus sylvatica L.); sessile (Quercus petraea (Matt.)
180
Liebl.) and/or pedunculate oaks (Quercus robur L.)] stands. Stands are even-aged.
181
At each site, samples representing five soil layers were obtained (0–10 cm, 10–20 cm, 20–40
182
cm, 40–80 cm and 80–100 cm; Fig. 1b). Samples of the first three top soil layers were
183
collected, at each depth, as 5 (replicates; pooled together on site) × 5 (sub-plots) sampling
184
points over the 5000 m2 central plot, by progressively digging a 50 cm wide soil profile
185
(Jonard et al. 2017; Ponette et al. 1997). This sampling campaign took place between 2007
186
and 2012. Samples of the two deeper soil layers were taken from two soil pits located just
187
outside the central plot and collected in 1994–1995 (Brêthes et al. 1997).
188
Bulk soils were air-dried and stored in plastic buckets right after sampling. One liter of soil of
189
each layer was retrieved for this study by isovolumetrically pooling the samples of the 5
190
subplots (200 mL each) for the first three layers (0–40 cm) and the 2 faces of the 2 soil pits
191
(250 mL each) for the two deepest layers (40–100 cm). The pooled samples were sieved at 2
192
mm before analysis.
193 194
b. Elemental analysis
195
Bulk < 2 mm-sieved soil samples were ground (< 250 µm; ultra-centrifugal mill ZM 200,
196
Retsch Gmbh) and organic carbon and total nitrogen concentrations were determined by dry
8
197
combustion with an elemental analyzer (CHN NA 1500, Carlo Elba). Samples with
198
carbonates (total CaCO3 = 3.5–835 g·kg−1) were first decarbonated following the same
199
protocol as Harris et al. (2001). Briefly, 30 mg of ground samples were weighed in 5 mm × 9
200
mm silver boats followed by the addition of 50 μL of distilled water. The boats were put in a
201
glass bell jar, next to a beaker containing 100 mL of concentrated HCl (12 mol·L−1). The air
202
in the jar was evacuated and samples let to sit in this HCl-saturated atmosphere to allow the
203
acid to dissolve water and hydrolyze the carbonates for 8 h. Then, the decarbonated samples
204
were dried at 60 °C in the silver boats for at least 48 h. Silver boats were further placed in 10
205
mm × 10 mm tin boats and analyzed for C and N.
206
POM fractions (see subsection d) were ground with a ball mill (mixer mill MM 200, Retsch
207
Gmbh) or a mortar and pestle when the sample mass was less than 0.05 g. Carbon
208
concentration was determined as for the bulk soil.
209 210
c. Respiration test
211
For each sample, 20 g of 2 mm-sieved soil were transferred in a 120 mL glass-flask and re-
212
humected at pF 2.5 (−0.033 MPa), which had been previously determined using a 5 Bar
213
pressure plate extractor (#1600, Soilmoisture Equipment Corp.). The flasks were fitted with
214
aluminum seals with PTFE-faced silicone septa to allow for headspace gas sampling and
215
placed inside an incubator (AE240 BIO EXPERT, Froilabo SAS) kept at 20 °C for 10 weeks
216
following a two-week period pre-incubation to allow the samples microbial activity to
217
stabilize (data not included).
218
Headspace gases were sampled at 1 to 2-week intervals during the 10-week incubation period
219
and CO2 concentrations were determined using an Agilent 490 micro-gas chromatograph
220
equipped with the OpenLAB Chromatography Data System EZChrom software.
9
221
When CO2 concentrations had reached 2.5–3% or was expecting to do so before the next
222
measurement, and/or when the cap had been pierced with the needle four times, flasks were
223
opened and flushed with fresh and moist air to return CO2 concentrations to ambient levels to
224
avoid anoxia (while maintaining the moisture content), before returning them to the incubator.
225
The CO2 released during the incubation experiment was expressed as the cumulated soil
226
microbial respiration rate (basal respiration) from the initial SOC content over the 10-week
227
period, or the 10-week mineralizable SOC (respired-C) and reported as a percentage of total
228
SOC to account for differences in the C content of the various layers and sites. Respired-C
229
was used as indicator of the highly labile SOC pool with mean residence time below 1 year.
230 231
d. Particle size and density SOC fractionation
232
To isolate the particulate organic matter (POM) fraction, samples were first dried at 50 °C for
233
24 h before weighing 25 g and transferred them in polyethylene (PE) 250 mL flasks. We then
234
added 180 mL of 0.5% sodium hexametaphostate solution and ten 5 mm-diameter glass beads
235
before shaking the samples overnight (50 rpm; 16 h) on an overhead shaker (Reax 2,
236
Heidolph). Samples were thoroughly rinsed over a 50-µm mesh with deionized water. The
237
sand fraction was then transferred back to a dry PE flask with a sodium polytungstate (SPT)
238
solution of density = 1.6 0.03 g·cm−3 (Crow et al. 2007; Golchin et al. 1994) and solution
239
was added up to around 180 mL. The flasks were shaken overhead by hand 10 times and
240
samples were left overnight to settle down after the cap of the flask was rinsed with the SPT
241
solution. The floating material was collected with a spatula and placed over a 50-µm mesh
242
sieve. If necessary some SPT solution was added back to the flask and the previous step was
243
repeated. This time, samples were placed in a centrifuge for 30 minutes to accelerate the
244
separation (2750 rpm or 1250 g). The floating material was again collected with the spatula or
245
pipetted depending on the amount left. This step was repeated if the light fraction was
10
246
abundant. If not, samples were left to settle down overnight before one last collection. The
247
POM fraction on the sieve was thoroughly rinsed with deionized water throughout the whole
248
process. The sieves and fractions were then placed in the oven at 50 °C for 24 h before being
249
weighed. To account for differences in the C content of the different samples, we calculated
250
the proportion of OC in the POM fraction (POM-C), expressed as a percentage of total SOC.
251
POM-C was used as indicator of the labile SOC pool with mean residence time generally up
252
to 20 years.
253 254
e. Thermal analysis: Rock-Eval 6
255
The thermal analysis of the samples was performed with a Rock-Eval 6 turbo device (Vinci
256
Technologies, France). Details about the equipment have been previously published (Behar et
257
al. 2001). We adapted the procedure developed for the analysis of SOM by Disnar et al.
258
(2003). Briefly, about 60 mg of ground sample were exposed to two consecutive thermal
259
treatments, first in a pyrolysis oven (200–650 °C; thermal ramping rate of 30 °C·min−1; under
260
N2 atmosphere) then in a combustion oven (300–850 °C; thermal ramping rate of
261
20 °C·min−1; under laboratory air atmosphere). At the beginning of the pyrolysis, there was an
262
isothermal step (at 200 °C) during ≈ 200 seconds during which the free hydrocarbons (HC)
263
were thermovaporized (S1 peak). The pyrolysis effluents (mostly HC) were detected and
264
quantified with flame ionization detection, while CO and CO2 were quantified by infrared
265
detection during both the pyrolysis and oxidation stages (Online Resource 1).
266
Two standard RE6 parameters describing SOC bulk chemistry were determined: the hydrogen
267
and oxygen indices (HI and OIRE6). The HI index corresponds to the amount of hydrocarbons
268
formed during thermal pyrolysis of the sample (HC evolved between 200 and 650 °C minus
269
the S1 peak) divided by the total SOC content of the sample and is expressed in mg HC·g−1
270
SOC. It describes the relative enrichment/depletion of SOC in hydrogen-rich moieties. The
11
271
OIRE6 index describes the relative oxidation status of SOC. It was calculated using the
272
equation proposed by Lafargue et al. (1998):
273
OIRE6 = 16 / 28 × OICO + 32 / 44 × OICO2
274
Where OICO2 corresponds to the CO2 yielded during thermal pyrolysis of the sample between
275
200 and 400 °C divided by the total SOC of the sample and OICO corresponds to the CO
276
yielded during thermal pyrolysis between 200 and 400–650 °C (wherever a minimum of CO
277
production is observed; in the absence of a minimum, the default upper-limit temperature is
278
set at 550 °C) divided by the total SOC of the sample. Thus OIRE6 is expressed in mg O2·g−1
279
SOC.
280
We derived two additional RE6 parameter describing the thermal stability of SOC: (i)
281
T50_HC_PYR, the temperature at which 50% of the HC resulting from the SOM pyrolysis had
282
evolved and (ii) T50_CO2_OX, the temperature at which 50% of the CO2 resulting from the SOM
283
oxidation had evolved. The upper limit temperature for the integration of this signal was set at
284
611 °C to obtain a total CO2 signal evolved from pure OM without interference of carbonates.
285
T50_HC_PYR was used as an indicator of the labile SOC pool with mean residence time
286
generally up to 20 years (negative correlation with the labile SOC pool according to
287
Gregorich et al. (2015) and Soucémarianadin et al. (2018)), while T50_CO2_OX was used as an
288
indicator of the stable SOC pool with mean residence time typically greater than 50–100
289
years, following Barré et al. (2016) and Cécillon et al. (2018). Signal processing of the RE6
290
thermograms, i.e., signal integration and calculation of T50_HC_PYR and T50_CO2_OX, was
291
performed with the R environment software v.3.3 (R Core Team 2016) using the hyperSpec
292
(Beleites and Sergo 2015) and pracma (Borchers 2015) R packages.
(equation 3)
293
12
294
f. Calculations and statistical analyses
295
For the respiration test, samples with very low C content (< 0.2%) were not considered as the
296
C respired during the incubation period was too close to the limit of detection for reliable
297
determination. For the thermal analysis, we used a C content threshold of 0.1% and manually
298
inspected the pyrolysis thermograms for samples with 0.1% ≤ C content ≤ 0.25% to make
299
sure of the validity of the RE6 data (by assessing the shape of the signal). This resulted in the
300
selection of n = 46 / 50 and n = 31 / 33 samples for the soil layers 40–80 cm and 80–100 cm
301
and the two methods respectively, leading to a total n = 236 for respiration test and n = 242
302
for RE6 (Fig. 1b). Because POM fractionation is time-consuming, we analyzed only the soil
303
layers 0–10 cm and 40–80 cm (Fig. 1b). At two sites, soil was too shallow (< 40 cm) and no
304
sample was therefore collected for the 40–80 cm layer, and we used the same C threshold as
305
for the RE6 to select the POM samples, which lead to n = 103. Out of the 236 samples
306
considered for the respiration test, 35 had a CaCO3 content over 5% (5.2–82.0%). We tested
307
the correlation between respired-C and CaCO3 content and, as it was not significant, decided
308
to proceed with the statistical analysis with all the samples.
309
Basic soil parameters (pH, texture, cation exchange capacity) were previously published in
310
Ponette et al. (1997). Average values are reported in Table 1 as well as the C content and C/N
311
ratio measured on the composite samples from this study. Because we used isovolumetric
312
pooled sampled, we saw appropriate to use average values of the 5 replicates × 5 sub-plots.
313
This was confirmed by the results we obtained for the C content (Online Resource 2).
314
Relationships between the indicators of SOC stability and soil physico-chemical properties as
315
well as climatic data (MAT and MAP) were estimated using Spearman rank correlation as the
316
data did not meet the assumption of normality. Correlations were also performed on different
317
sets of samples for the different indicators (233 samples were included for the respiration test,
318
242 for the RE6 comparison and 103 for the POM fractionation).
13
319
A principal component of analysis (PCA) was performed to illustrate linear relations between
320
the indicators derived from the 3 methods at two different depths: 0–10 cm and 40–80 cm.
321
For that purpose, data were log-transformed, centred and scaled. To determine the number of
322
principal components to select, we looked at the percentage of the total variance explained
323
and used a scree plot and Kaiser’s criterion. We projected the physico-chemical and climatic
324
variables on the circle of correlations to see if any of those was associated with either of the
325
principal components and could thus be associated with SOC stability.
326
The statistical analysis to determine the driving factors of SOC stability was performed in two
327
steps: first over the complete soil profile and then on each soil layer individually. We used
328
multivariate models to assess the effects of the different environmental factors on the RE6-
329
based parameters and respiration test and POM fractionation results. For this “analysis by soil
330
profile”, we used linear mixed models introducing a random intercept for each site (≈ to treat
331
“site” as random effect) to take into account that the different layers constituted repeated
332
measures (increasing depth within a same RENECOFOR site). To do so we added the
333
compound symmetry structure, which is similar to the variance structure of random-intercept-
334
only model, to a generalized least squares function (that fits a linear model using generalized
335
least squares; (Pinheiro et al. 2016)). Model selection was then implemented with a top-down
336
strategy. The response variables were transformed, to the exception of T50_HC_PYR and
337
T50_CO2_OX, using the Box-Cox transformation technique (log10 for POM-C and (respired-C +
338
1)), as they showed evidence of the variance increasing with the mean response. After
339
transformation, the residuals followed an approximate normal distribution.
340
To explore further the effects of soil classes, vegetation types and climatic zones within each
341
layer, we then conducted three-way analyses of variance (ANOVA) “by layer” with type II
342
analysis, when the interactions were not significant. Data were not transformed except for
343
respired-C. Multiple comparison tests were performed with Tukey's honest significant
14
344
differences (to get adjusted p-values for all comparisons) and pairwise t-test (no adjustment
345
method). All comparisons were considered significant at an alpha value () of 0.05.
346
All statistical analyses were performed using the R 3.3 statistical software (R Core Team
347
2016) with the factoextra (Kassambara and Mundt 2016), nlme (Pinheiro et al. 2016), lme4
348
(Bates et al. 2015) and car (Fox and Weisberg 2011) packages.
349
350
Results
351
a. Highly labile SOC pool: respired-C
352
Depth and soil class significantly influenced variations in soil basal respiration (respired-C)
353
across our 53 study plots. The depth × soil interaction was also included in the selected model
354
(p = 0.042; Online Resource 3). The respired-C fraction was on average 1.46 0.63% of total
355
SOC (Online Resource 4) with no significant differences among vegetation types (Fig. 3) or
356
climatic zones (Table 2). Respired-C decreased with depth (Fig. 2) but that factor was only
357
marginally significant. Soil class was not significant in the 40–80 cm layer but otherwise entic
358
Podzols had significantly lower respired-C than the two other soil classes (p = 0.0010–0.042;
359
Table 2; Fig. 4).
360 361
b. Labile SOC pool: POM-C and T50_HC_PYR
362
Only depth and soil type significantly affected variations in POM-C across our 53 study plots
363
(Online Resource 3). The labile SOC fraction contained in POM decreased by almost half
364
from the 0–10 cm layer to the 40–80 cm layer (with respective proportions of 22.6 7.3% and
365
11.5 6.2% of total SOC; Fig. 2). The analysis by layer confirmed that neither climate nor
366
vegetation significantly influenced POM-C variations (Table 2; Fig. 3). Eutric Cambisols had
15
367
significantly less POM-C than entic Podzols in the surface layer and dystric Cambisols at 40–
368
80 cm (Fig. 4; Online Resource 4).
369
Depth, vegetation type and soil class influenced variations in T50_HC_PYR, the RE6-derived
370
temperature at which 50% of the HC resulting from the SOM pyrolysis had evolved. The
371
three interactions depth × soil, depth × veg and soil × veg were also included in the selected
372
model (Online Resource 3). T50_HC_PYR significantly increased with depth (422 8 to 452
373
13 °C at 0–10 cm and 80–100 cm, respectively; Fig. 2), illustrating the decrease of the labile
374
SOC pool with increasing depth. Eutric Cambisols had significantly higher T50_HC_PYR than
375
dystric Cambisols and entic Podzols in the surface layer but had significantly lower
376
T50_HC_PYR than the two other soil classes at 20–40 cm depth (Fig. 4; Online Resource 4).
377
Moreover, in the surface layer, samples in deciduous plots had a significantly higher
378
T50_HC_PYR than those in coniferous plots (427 9 and 417 7 °C, respectively, p < 0.001;
379
Table 2; Fig. 3).
380 381
c. Stable SOC pool: T50_CO2_OX
382
Depth, vegetation and climate induced significant variations in the temperature at which 50%
383
of the CO2 resulting from the SOM oxidation had evolved (T50_CO2_OX) across our 53 study
384
plots. The depth × climate interaction was also included in the selected model (Online
385
Resource 3).
386
T50_CO2_OX increased with depth (from 399 9 to 437 19 °C at 0–10 cm and 80–100 cm,
387
respectively; Fig. 2). Vegetation type was significant only in the top soil layers (0–40 cm)
388
with samples from coniferous plots having a lower T50_CO2_OX than those in deciduous plots
389
(395 6 °C and 405 9 °C, respectively; Fig. 3). Soil class was a significant factor both in
390
layers 0–10 cm (p = 0.0085) and 40–80 cm (p = 0.0489; Table 2) but with contrasting trend:
391
in the surface layer, eutric Cambisols had the highest T50_ CO2_OX (significantly higher than 16
392
entic Podzols) and the lowest T50_ CO2_OX in the 40–80 cm layer (significantly lower than the
393
dystric Cambisols; Fig. 4; Online Resource 4). Climate was a significant factor in all layers:
394
over the whole profile (0–100 cm), T50_ CO2_OX was lower in mountainous plots than in plots
395
located in the two other climate classes (p ≤ 0.0001–0.0159; Table 2; Fig. 5).
396 397
d. Correlations between soil and climate characteristics and the indicators of SOC stability
398
There were only a few significant and strong correlations between the indicators of SOC
399
stability and soil physico-chemical properties (Table 3). Notably POM-C and T50_HC_PYR, the
400
two indicators of the labile SOC pool, had strong and opposite correlations with HI (ρ = 0.67
401
and −0.67 respectively) and OIRE6 (ρ = −0.76 and 0.63 respectively). POM-C was also
402
positively correlated with C/N ratio (ρ = 0.61). T50_CO2_OX was negatively correlated to the C
403
content (ρ = −0.72; Table 3). Respired-C showed no strong correlation with soil or climate
404
characteristics. In our samples we observed no strong correlation for the four indicators of
405
SOC stability with soil texture, pH (although ρ = −0.54 with POM-C) or the climatic
406
characteristics (MAT or MAP; Table 3).
407
Because of the strong “depth effect” on each indicator of SOC stability, we explored the
408
evolution of these correlations within each soil layer and noticed that they also evolved with
409
depth (Online Resource 5). To describe the similarity or dissimilarity in the different
410
indicators of SOC lability, we conducted a principal component analysis (PCA) at the two
411
depths for which POM-C was available (0−10 and 40−80 cm). In the 0−10 cm layer, the first
412
two principal components (PC) explained almost 60% of the total variance (Fig. 6). PC1
413
clearly separated soil samples dominated by highly labile SOC pool from those dominated by
414
labile SOC pool associated with POM-C. Indeed, along PC1, POM-C and respired-C showed
415
moderate to strong negative and positive loadings respectively, while T50_CO2_OX had moderate
416
positive loadings (Fig. 6). T50_HC_PYR showed strong negative loadings along PC2, while it had
17
417
very weak negative loadings along PC1. Results were quite different in the 40−80 cm layer,
418
where the first two principal components (PC) explained approximately 63% of the total
419
variance (Fig. 6). In these deeper samples, PC1 tightly grouped soil samples with high
420
proportion of highly labile SOC pool (respired-C) and those with high proportion of labile
421
SOC pool associated with POM-C with strong positive loadings for both indicators along
422
PC1. T50_CO2_OX and T50_HC_PYR had both strong positive loadings along PC2, while they had
423
very weak loadings along PC1.
424
SOC content was not related to any of the indicators of SOC stability in the surface layer,
425
while it was moderately and negatively correlated with POM-C, respired-C and T50_CO2_OX in
426
the deep layer (Fig. 6; Online Resource 5). In the surface layer, pH was associated with
427
positive values on the first PC (high respired-C), while sand content and soil C/N ratio were
428
associated with negative values on the first PC (high POM-C; Fig. 6; Online Resource 5). HI
429
and OIRE6 were well correlated to the indicators of highly labile and labile SOC pools,
430
specifically in the surface layer. Correlations of the physico-chemical variables with POM-C
431
were slightly lower at depth (Online Resource 5), but below 20 cm depth, all these
432
correlations with T50_HC_PYR and respired-C (directly with the indicators or with the PCs) had
433
greatly decreased or even disappeared (Online Resource 5). Conversely, T50_CO2_OX was not
434
more than weakly correlated with the physico-chemical parameters over the whole profile but
435
its positive correlation with MAT tended to be higher in deeper layers (Online Resource 5;
436
Fig.6).
437
The PCA biplot displaying the samples based on their soil class (Fig. 6) showed a difference
438
between eutric Cambisol and entic Podzol with the two first PCs in the surface layer (0–10
439
cm): samples of eutric Cambisols had higher respired-C, lower POM-C and generally higher
440
T50_HC_PYR and T50_CO2_OX than those of entic Podzols.
18
441
In the deep layer (40–80 cm), the two PCs separated samples of dystric Cambisols from
442
samples of eutric Cambisols. The former were mostly characterized by high respired-C and
443
POM-C values and high values of T50_HC_PYR and T50_CO2_OX. The latter had either high values
444
of T50_HC_PYR and T50_CO2_OX with low respired-C and POM-C or low values of T50_HC_PYR and
445
T50_CO2_OX with high respired-C and POM-C. The second PC separated samples that had more
446
stable SOC (high values on PC2) from those that had less stable SOC (low values on PC2).
447
Dystric Cambisols thus appeared as having more stable SOC than the two other soil classes
448
(Fig. 6).
449
450
Discussion
451
a. Depth is the most discriminating factor of SOC stability
452
In our study sites, depth was the most discriminating factor, affecting significantly all
453
indicators of SOC stability. Indeed, with depth, we observed consistent trends for the
454
indicators of the highly-labile (decrease of respired-C) and the labile (decrease of POM-C and
455
increase of T50_HC_PYR) SOC pools, and an opposite trend for the indicator of the stable SOC
456
pool (increase of T50_CO2_OX), verifying our first hypothesis.
457
Studies have shown a decrease in POM-C (% of total SOC) with increasing depth down to
458
20–30 cm (Hassink 1995; Schrumpf and Kaiser 2015), down to 50 cm (Diochon and Kellman
459
2009) or down to > 140 cm (Cardinael et al. 2015; Moni et al. 2010). Previous studies have
460
also reported decreasing respired-C with depth during incubations of variable duration (e.g.,
461
Dodla et al. 2012; Gillespie et al. 2014; Schrumpf et al. 2013; Wang and Zhong 2016 with 12
462
days at 22.5 °C, 20 days at 15 °C, 98 days at 25 °C, 60 days at 25 °C, respectively).
463
Variations in soil basal respiration with depth have been related with variations in C dynamics
464
(e.g., Agnelli et al. 2004; Salomé et al. 2010; Wordell-Dietrich et al. 2017).
19
465
Labile SOC content usually decreases while stable SOC increases with depth (e.g., Jenkinson
466
et al. 2008; Lorenz and Lal 2005; Mathieu et al. 2015) and this is correlated with longer SOC
467
turnover rates as exemplified by Torn et al. (1997) and Mathieu et al. (2015) who showed
468
strong effects of depth on SOC mean age.
469 470
b. Soil class as a major factor controlling SOC stability
471
Soil class had significant effects on the indicators of the highly-labile (respired-C) and labile
472
(POM-C and T50_HC_PYR) SOC pools. Contrary to our third hypothesis, these soil effects were
473
not limited to the deeper layers and were indeed present in the surface layer for all four
474
indicators of SOC stability (Table 2).
475
i) Modulation of the effect of depth by soil class
476
The effect of depth on SOC stability, i.e. the decrease of the labile SOC and concomitant
477
increase in stable SOC was modulated by the soil class. First, the surface (0-10 cm) values of
478
all SOC stability parameters varied among soil classes (Table 2; Fig. 4), surface layers of
479
eutric Cambisols being generally enriched in stable SOC compared to other soil classes. This
480
might be explained by a relative higher stabilization of SOC in the surface layer of the eutric
481
Cambisols that could be due to a faster cycling in relation to lower C/N ratios (13.2 1.5 vs.
482
18.4 4.5 for the other two classes) and higher pH (6.2 0.9 vs. 4.3 0.3 for the other two
483
classes), stimulating the mineralization of the more labile SOC and resulting in a more stable
484
SOC overall. SOC stabilisation through Ca-mediated processes (occlusion, inclusion,
485
sorption; Rowley et al., 2018) may also explain the higher SOC stability in surface layers of
486
eutric Cambisols.
487
Then the amplitude of the evolution of SOC stability with depth varied among soil classes
488
(Figure 4). Thus, the higher stability observed in the surface layer of eutric Cambisols had
489
disappeared by 20–40 cm depth. This modulation of the effect of depth by soil class could be
20
490
linked to different types of SOM moieties developed by very different pedogenetic processes,
491
eutric Cambisols showing a relatively more oxidized SOC than other soil classes (higher
492
OIRE6; specifically down to 40 cm depth). In the deep layer, dystric Cambisols were
493
characterized by high OIRE6 values, which could be linked to larger stable SOC pools in this
494
soil class, likely associated with more oxidized SOC moieties (Cécillon et al., 2018).
495
Lastly, in our sites, soil class did not significantly affect the indicator of stable SOC
496
(T50_CO2_OX; at least not for the whole profile model), and the stable SOC pool appeared
497
mostly driven by differences in MAT (specifically at depth; Fig 6; see section d. of the
498
Discussion). This result is seemingly contradictory to the findings of Mathieu et al. (2015)
499
who reported a strong influence of soil type on deep soil mean carbon age. It should be noted
500
that these authors covered a greater soil variability in their study and if we focus on the 3 soil
501
classes considered in our work, their results are similar to ours (i.e., no large difference among
502
the three soil classes).
503
ii) Soil variables explaining the pedological effect on SOC stability
504
Soil type is not often used as an explanatory factor of variations in SOC quality/stability (e.g.,
505
Wiesmeier et al. 2014) and physico-chemical properties (e.g., clay content, pH, etc.) are often
506
preferred (e.g., Tian et al. 2016). We thus wondered whether a series of physico-chemical
507
parameters could have summarized the soil class effect on SOC stability.
508
The effect of soil type on the highly-labile and labile SOC pools may be due to differences in
509
soil texture (sand content), pH or C/N ratio (Online Resource 6, Fig. 6). Indeed, a strong
510
effect of soil texture on SOC stability in the topsoil (0–10 cm) has previously been reported in
511
the literature. For instance, just like we did (POM-C in coarse-textured entic Podzols = 25.1
512
7.6% vs. 19.2 5.6% in fine-textured eutric Cambisols; Fig. 4; Online Resource 4), several
513
studies have observed a trend of more labile SOC (expressed as POM-C or size of the
21
514
intermediate SOC pool) in coarser soils (Schiedung et al. 2017; Wiesmeier et al. 2014) or
515
directly linked to the sand fraction (Tian et al. 2016; Vos et al. 2017).
516
In our sites, respired-C was higher in fine-textured soils up to 40 cm depth and was
517
significantly lower in Podzols. Conversely, several studies have reported higher C
518
mineralization rates in sandy soils than in finer-textured soils in various contexts from boreal
519
forests through croplands in Norway and all the way to Brazil (Bauhus et al. 1998; Frøseth
520
and Bleken 2015; Schmatz et al. 2017). These opposite results could originate from various
521
sources, and specifically differences in C/N ratio. For our sites, the topsoil C/N ratio in eutric
522
Cambisols was significantly lower (13.3 1.5) than in entic Podzols (19.9 5; Online
523
Resource 4), which could affect the microbial use efficiency during the incubation (e.g.,
524
Cotrufo et al. 1995). Differences in pH could be another good explanation. Our entic Podzols
525
and eutric Cambisols had lower and higher pH than the till (≈ sandy) and clay soils from the
526
Bauhus et al. (1998) study, respectively. It has been shown that a slight increase in pH could
527
significantly increase rates of mineralization (Curtin et al. 1998).
528
All these physico-chemical variables reflect the importance of SOM stoichiometry (C/N ratio)
529
(Ågren et al. 2013; Cleveland and Liptzin 2007) and substrate accessibility (reduced
530
protection via aggregation in sandy soils or increase in dissolved OM with higher pH) for its
531
degradation (Dungait et al. 2012; Schmidt et al. 2011). However the lack of or low to
532
moderate correlations between the different indicators of SOC stability and these soil physico-
533
chemical parameters (texture and pH respectively; Table 3) suggest that there is not one
534
characteristics only responsible for the soil effect we observed or that, at least, they are not
535
valid at all depths of the soil profile as we have shown (Online Resource 5; Fig. 6). There are
536
likely complex interactions, reflecting pedogenetic processes behind this and, in that regard,
537
the soil class is integrative and goes beyond simple soil physico-chemical characteristics, and
538
is thus capable of reflecting variations in SOC stability.
22
539 540
c. Vegetation type mostly affects SOC stability in topsoils
541
In our study sites, the effect of vegetation type (coniferous forest vs. deciduous forest) on
542
SOC stability was concentrated on the surface layer (0–10 cm), thus validating part of our
543
second hypothesis. Vegetation type significantly influenced both thermal indicators of SOC
544
stability in surface soil layers while the classical indicators of the highly labile (respired-C)
545
and the labile (POM-C) SOC pools were not affected by vegetation.
546
Effects of vegetation on the labile SOC pool have been previously reported, but they were
547
mainly observed at the tree species level (Bauhus et al. 1998; Augusto et al. 2002; Hobbie et
548
al. 2007; Olsen and Van Miegroet 2010; Laganière et al. 2012; Vesterdal et al. 2012; You et
549
al. 2016). Conversely, previous studies have also reported a lack of difference in the highly
550
labile SOC pool (estimated by respired-C) of topsoils in deciduous and coniferous stands
551
(Fissore et al. 2008; Van Miegroet et al. 2005).
552
In our study sites, the surface layer (0–10 cm) of coniferous stands had more labile SOC
553
(lower T50_HC_PYR) but also less stable SOC (lower T50_CO2_OX) than in deciduous stands,
554
validating the second part of our second hypothesis. Similar findings were reported in
555
Bavarian forests, where deciduous and mixed stands showed smaller labile SOC and larger
556
stable SOC pools than coniferous stands (Wiesmeier et al. 2014).
557
Deciduous forests indeed tend to rely on a more rapid nutrient cycling between soil and plant
558
(Cole and Rapp 1981). Quideau et al. (2001) showed that oak-derived SOM has undergone
559
extensive oxidation compared with the litter, while SOM under coniferous vegetation became
560
enriched in recalcitrant alkyl C. The authors conclude that deciduous stands were
561
characterized by a high microbial activity and rapid nutrient release whereas the accumulated
562
SOM in coniferous forests had a low bioavailability. The higher pH values of the litter in
563
deciduous stands favour bioturbation and incorporation of OM in surface mineral soil,
564
whereas the more acidic coniferous litter accumulates in the organic layers (e.g., Wiesmeier et 23
565
al. 2013). These results could also be explained by lower C/N ratios in deciduous plots (e.g.,
566
Cools et al. 2014). C/N ratio in deciduous stands (15.0 2.8) were indeed lower than in
567
topsoils under coniferous (18.4 5.1) and closer to that of the microbial biomass. This
568
difference in C/N ratios between the two vegetation types was more drastic when considering
569
the plant inputs (deciduous = 46.5 9.5; coniferous = 60.9 16.8; data not shown) and high
570
C/N ratios in litter are often associated with low decomposition rates (Melillo et al. 1982;
571
Norby et al. 2001; Tian et al. 2016). This would result in a higher litter mineralization
572
potential in deciduous stands and because the highly labile/labile pool is utilized more readily
573
in these plots (higher litter C turnover), it would result in a smaller size of the labile pool in
574
deciduous stands and thus a higher T50_HC_PYR. Indeed, there was a negative and moderate
575
correlation between T50_HC_PYR and the inputs C/N ratio, but only in the top layer (Online
576
Resource 5). In the long term, the low C/N ratio of the deciduous litters could also explain the
577
higher T50_CO2_OX through higher SOC stabilization (Berg 2000). This highlights the
578
importance of the bulk chemistry of SOC inputs (Hobbie et al. 2007; You et al. 2016) for
579
SOC cycling. This difference in SOC stability (in the mineral soil) between the two
580
vegetation types has also been mentioned in the review by Augusto et al. (2015) and the
581
reasons of this difference identified as a future research need.
582
The limited effect of vegetation types in our study sites could be linked to species
583
heterogeneity within the two vegetation types and this might be an important limitation of this
584
work. We chose to consider vegetation types and not tree species to obtain a more balanced
585
design (29 plots in coniferous stands and 24 in deciduous stands; Fig. 1a) and our deciduous
586
stands included both beech and oak-dominated forests. Inter-species variations in terms of
587
their characteristics (e.g., aboveground litter composition; roots) and their effects on the soil
588
could explain, at least partially, the limited effects of the (broad) vegetation classes in this
589
study. Some studies have indeed reported an effect of tree species on both in-situ and 24
590
laboratory soil respiration rates (measured over a year) (Hobbie et al. 2007; Vesterdal et al.
591
2012). In oak stands, the respiration rate was greater than in beech stands, but similar to those
592
in spruce stands, illustrating that the deciduous/coniferous dichotomy might be masking some
593
species effects, at least on the labile SOC pool, but quite likely also on the stable SOC pool.
594 595
d. Climatic control of the stable SOC pool
596
In our study, climate effects on SOC stability were concentrated on the stable SOC pool. Soils
597
located in plots with mountainous climate had higher C content (data not shown) than those in
598
plots in regions with oceanic or continental influence. However this higher concentration was
599
not associated with climate effects on the labile SOC indicators. Nevertheless climate was a
600
strong driver of the stable SOC indicator, SOC being less stable (lower T50_ CO2_OX) in
601
mountainous plots. Our last hypothesis (SOC in mountainous plots would be more labile) was
602
thus only partially verified.
603
In Bavarian forests (Wiesmeier et al. 2014), the passive SOC pool (roughly equivalent to our
604
stable SOC pool) was negatively related to MAP, which agrees with our results as the
605
mountainous plots were the wettest (1323 297 mm) and there were negative correlations
606
between MAP and T50_CO2_OX in almost all layers (Online Resource 5). However, unlike us,
607
Wiesmeier et al. (2014) also detected a strong climate effect on the labile SOC as the latter
608
was under the control of both temperature and precipitation, and the most labile SOC was
609
found in mountainous regions. Similarly, Meier and Leuschner (2010) reported more labile
610
SOC when temperature decreased and precipitation increased, while Leifeld et al. (2009)
611
reported more POM-C at higher elevation in grasslands. In our study sites, there were no
612
more than weak correlations between our labile SOC pools and MAT and MAP, even when
613
considering individual layers (Online Resource 5). Nevertheless, it should be noted that the
614
mean elevation of our mountainous plots was 1230 m ( 280 m) while Leifeld et al. (2009)
25
615
had 5 out of their 8 sites located at ≥ 1410 m elevation. Finally, this “high elevation” effect on
616
the labile fraction, expressed as POM-C requires caution as, in mountainous regions, lower
617
MAT tend to reduce microbial activity thus favouring SOC accumulation (e.g., Tewksbury
618
and Van Miegroet 2007), even in tropical areas (Araujo et al. 2017). In cold environments, the
619
residence time of this “labile” (as very close to the litter inputs) SOC is much longer than in
620
more temperate climate (Leifeld et al. 2009). In that particular context, the relationship
621
between thermal stability and SOC residence time/turnover may also be questioned and
622
requires further study.
623
Another possible limitation of the present study is that vegetation and climate appeared to be
624
confounded factors in our design with coniferous plots being preferentially found in
625
mountainous regions: our coniferous plots had a mean elevation of 831 m ( 476 m) while it
626
was 511 m ( 413 m) in the deciduous plots. This had an incidence on the MAT especially
627
(Online Resource 6).
628 629
630
Conclusions
631
In this study, thanks to a large set of forest soil samples with contrasted SOC stability and the
632
use of several indicators, we were able to highlight the influence of four environmental factors
633
on SOC stability: depth, soil, vegetation and climate; with the degree of significance of these
634
factors (and their interactions) varying among the SOC pools.
635
Our results show that pedology is a discriminant factor of SOC stability, more than individual
636
soil physico-chemical attributes. Soil type constitutes an integrated parameter that might be an
637
efficient way to capture SOC turnover properties. Upon modification in land management that
638
would result in a decrease of C inputs to the soil, our results let suggest that the SOC of eutric
26
639
Cambisols may be less sensitive than the one of dystric Cambisols but specifically of entic
640
Podzols that may be more prone to losses.
641
To conclude, soil class, vegetation type and climatic zone all had a significant influence on
642
SOC stability at various depths in our studied French forest soils and these environmental
643
factors should thus be included in models estimating the ecosystem service of climate
644
regulation.
645 646
647
Acknowledgements
648
This work was supported by the French Environment and Energy Management Agency
649
(ADEME) [APR REACCTIF, piCaSo project] and Campus France [PRESTIGE-2015-3-
650
0008]. We thank M. Bryant, S. Cecchini, J. Mériguet, F. Savignac, and L. Le Vagueresse for
651
their technical support.
652
653
References
654
Agnelli A, Ascher J, Corti G, Ceccherini MT, Nannipieri P, Pietramellara G (2004)
655
Distribution of microbial communities in a forest soil profile investigated by microbial
656
biomass, soil respiration and DGGE of total and extracellular DNA. Soil Biol Biochem. doi:
657
10.1016/j.soilbio.2004.02.004
658
Ågren GI, Hyvönen R, Berglund SL, Hobbie SE (2013) Estimating the critical N:C from
659
litter decomposition data and its relation to soil organic matter stoichiometry. Soil Biol
660
Biochem. doi: 10.1016/j.soilbio.2013.09.010
661 662
Amundson R (2001) The Carbon Budget in Soils. Annu Rev Earth Planet Sci 29:535-562. doi: 10.1146/annurev.earth.29.1.535 27
663
Araujo MA, Zinn YL, Lal R (2017) Soil parent material, texture and oxide contents have
664
little effect on soil organic carbon retention in tropical highlands. Geoderma. doi:
665
10.1016/j.geoderma.2017.04.006
666 667 668
Augusto L, Ranger J, Binkley D, Rothe A (2002) Impact of several common tree species of European temperate forests on soil fertility. Ann For Sci 59:233-253 Augusto L, De Schrijver A, Vesterdal L, Smolander A, Prescott C, Ranger J (2015)
669
Influences of evergreen gymnosperm and deciduous angiosperm tree species on the
670
functioning of temperate and boreal forests. Biol Rev 90:444-466. doi: 10.1111/brv.12119
671
Balesdent J (1996) The significance of organic separates to carbon dynamics and its
672
modelling in some cultivated soils. Eur J Soil Sci 47:485-493. doi: 10.1111/j.1365-
673
2389.1996.tb01848.x
674
Barré P, Durand H, Chenu C, Meunier P, Montagne D, Castel G, Billiou D,
675
Soucémarianadin L, Cécillon L (2017) Geological control of soil organic carbon and nitrogen
676
stocks at the landscape scale. Geoderma 285:50-56. doi: 10.1016/j.geoderma.2016.09.029
677
Barré P, Plante AF, Cécillon L, Lutfalla S, Baudin F, Christensen BT, Eglin T, Fernandez
678
JM, Houot S, Kätterer T, Le Guillou C, Macdonald A, van Oort F, Chenu C (2016) The
679
energetic and chemical signatures of persistent soil organic matter. Biogeochemistry 130:1-
680
12. doi: 10.1007/s10533-016-0246-0
681 682 683
Bates D, Mächler M, Bolker B, Walker S (2015) Fitting Linear Mixed-Effects Models Using lme4. Journal of Statistical Software, Articles 67:1-48 Bauhus J, Paré D, Côté L (1998) Effects of tree species, stand age and soil type on soil
684
microbial biomass and its activity in a southern boreal forest. Soil Biol Biochem. doi:
685
10.1016/S0038-0717(97)00213-7
686 687
Behar F, Beaumont V, Penteado DB (2001) Rock-Eval 6 Technology: Performances and Developments. Oil Gas Sci Technol 56:111-134. doi: 10.2516/ogst:2001013
28
688
Beleites C, Sergo V (2015) hyperSpec: a package to handle hyperspectral data sets in R
689
Berg B (2000) Litter decomposition and organic matter turnover in northern forest soils.
690
For Ecol Manage 133:13-22. doi: 10.1016/S0378-1127(99)00294-7
691
Borchers HW (2015) pracma: Practical numerical math functions
692
Brêthes A, Ulrich E, Lanier M (1997) RENECOFOR : caractéristiques pédologiques des
693
102 peuplements du réseau : observations de 1994/95. Office national des forêts, Département
694
des recherches techniques, Fontainebleau, France
695
Camino-Serrano M, Gielen B, Luyssaert S, Ciais P, Vicca S, Guenet B, Vos BD, Cools N,
696
Ahrens B, Altaf Arain M, Borken W, Clarke N, Clarkson B, Cummins T, Don A, Pannatier
697
EG, Laudon H, Moore T, Nieminen TM, Nilsson MB, Peichl M, Schwendenmann L, Siemens
698
J, Janssens I (2014) Linking variability in soil solution dissolved organic carbon to climate,
699
soil type, and vegetation type. Glob Biogeochem Cycles 28:497-509. doi:
700
10.1002/2013GB004726
701
Canadell JG, Le Quéré C, Raupach MR, Field CB, Buitenhuis ET, Ciais P, Conway TJ,
702
Gillett NP, Houghton RA, Marland G (2007) Contributions to accelerating atmospheric CO2
703
growth from economic activity, carbon intensity, and efficiency of natural sinks. PNAS
704
104:18866-18870. doi: 10.1073/pnas.0702737104
705
Cardinael R, Chevallier T, Barthès BG, Saby NPA, Parent T, Dupraz C, Bernoux M,
706
Chenu C (2015) Impact of alley cropping agroforestry on stocks, forms and spatial
707
distribution of soil organic carbon — A case study in a Mediterranean context. Geoderma
708
259–260:288-299. doi: 10.1016/j.geoderma.2015.06.015
709
Carter MR, Gregorich EG, Angers DA, Donald RG, Bolinder MA (1998) Organic C and N
710
storage, and organic C fractions, in adjacent cultivated and forested soils of eastern Canada.
711
Soil Tillage Res. doi: 10.1016/S0167-1987(98)00114-7
29
712
Cécillon L, Baudin F, Chenu C, Houot S, Jolivet R, Kätterer T, Lutfalla S, Macdonald A,
713
van Oort F, Plante AF, Savignac F, Soucémarianadin L, Barré P (2018) A model based on
714
Rock-Eval thermal analysis to quantify the size of the centennially persistent organic carbon
715
pool in temperate soils. Biogeosciences Discussions 2018:1-25. doi: 10.5194/bg-2018-15
716 717 718 719 720 721 722
Cleveland CC, Liptzin D (2007) C:N:P stoichiometry in soil: is there a "Redfield ratio" for the microbial biomass?. Biogeochemistry 85:235-252. doi: 10.1007/s10533-007-9132-0 Cole DW, Rapp M (1981) Elemental cycling in forest ecosystems. In: Reichle DE (ed) Dynamic Properties of Forest Ecosystems. Cambridge University Press, pp 341-409 Coleman K, Jenkinson D (1999) RothC-26.3. A Model for the Turn-over of Carbon in Soils. Model Description and Windows Users Guide. IACR – Rothamsted, Harpenden. Cools N, Vesterdal L, De Vos B, Vanguelova E, Hansen K (2014) Tree species is the
723
major factor explaining C:N ratios in European forest soils. Forest Ecol Manag. doi:
724
10.1016/j.foreco.2013.06.047
725 726 727
Cotrufo FM, Ineson P, Roberts DJ (1995) Decomposition of birch leaf litters with varying C-to-N ratios. Soil Biol Biochem. doi: 10.1016/0038-0717(95)00043-E " Crow SE, Swanston CW, Lajtha K, Brooks JR, Keirstead H (2007) Density fractionation
728
of forest soils: methodological questions and interpretation of incubation results and turnover
729
time in an ecosystem context. Biogeochemistry 85:69-90. doi: 10.1007/s10533-007-9100-8
730
Curtin D, Campbell CA, Jalil A (1998) Effects of acidity on mineralization: pH-
731
dependence of organic matter mineralization in weakly acidic soils. Soil Biol Biochem. doi:
732
10.1016/S0038-0717(97)00094-1
733
Diochon AC, Kellman L (2009) Physical fractionation of soil organic matter:
734
Destabilization of deep soil carbon following harvesting of a temperate coniferous forest. J
735
Geophys Res 114. doi: 10.1029/2008JG000844
30
736
Disnar JR, Guillet B, Keravis D, Di-Giovanni C, Sebag D (2003) Soil organic matter
737
(SOM) characterization by Rock-Eval pyrolysis: scope and limitations. Org Geochem 34:327-
738
343. doi: 10.1016/S0146-6380(02)00239-5
739
Dodla SK, Wang JJ, DeLaune RD (2012) Characterization of labile organic carbon in
740
coastal wetland soils of the Mississippi River deltaic plain: Relationships to carbon
741
functionalities. Sci Total Environ 435–436:151-158. doi: 10.1016/j.scitotenv.2012.06.090
742
Dungait JAJ, Hopkins DW, Gregory AS, Whitmore AP (2012) Soil organic matter
743
turnover is governed by accessibility not recalcitrance. Glob Change Biol 18:1781-1796. doi:
744
10.1111/j.1365-2486.2012.02665.x
745
Feng W, Shi Z, Jiang J, Xia J, Liang J, Zhou J, Luo Y (2016) Methodological uncertainty
746
in estimating carbon turnover times of soil fractions. Soil Biol Biochem 100:118-124. doi:
747
10.1016/j.soilbio.2016.06.003
748
Fissore C, Giardina CP, Kolka RK, Trettin CC, King GM, Jurgensen MF, Barton CD,
749
McDowell SD (2008) Temperature and vegetation effects on soil organic carbon quality along
750
a forested mean annual temperature gradient in North America. Global Change Biol 14:193-
751
205. doi: 10.1111/j.1365-2486.2007.01478.x
752
Fox J, Weisberg S (2011) An R Companion to Applied Regression
753
Frøseth RB, Bleken MA (2015) Effect of low temperature and soil type on the
754
decomposition rate of soil organic carbon and clover leaves, and related priming effect. Soil
755
Biol Biochem. doi: 10.1016/j.soilbio.2014.10.004
756
Gillespie AW, Sanei H, Diochon A, Ellert BH, Regier TZ, Chevrier D, Dynes JJ, Tarnocai
757
C, Gregorich EG (2014) Perennially and annually frozen soil carbon differ in their
758
susceptibility to decomposition: Analysis of Subarctic earth hummocks by bioassay, XANES
759
and pyrolysis. Soil Biol Biochem 68:106-116. doi: 10.1016/j.soilbio.2013.09.021
31
760
Golchin A, Oades JM, Skjemstad JO, Clarke P (1994) Study of free and occluded
761
particulate organic matter in soils by solid state 13C CP/MAS NMR spectroscopy and
762
scanning electron microscopy. Aust J Soil Res 32:285-309. doi: 10.1071/SR9940285
763
Gregorich EG, Gillespie AW, Beare MH, Curtin D, Sanei H, Yanni SF (2015) Evaluating
764
biodegradability of soil organic matter by its thermal stability and chemical composition. Soil
765
Biol Biochem 91:182-191. doi: 10.1016/j.soilbio.2015.08.032
766
Harris D, Horwáth WR, van Kessel C (2001) Acid fumigation of soils to remove
767
carbonates prior to total organic carbon or CARBON-13 isotopic analysis. Soil Sci Soc Am J
768
65:1853-1856. doi: 10.2136/sssaj2001.1853
769
Hassink J (1995) Density fractions of soil macroorganic matter and microbial biomass as
770
predictors of C and N mineralization. Soil Biol Biochem 27:1099-1108. doi: 10.1016/0038-
771
0717(95)00027-C
772
Hobbie SE, Ogdahl M, Chorover J, Chadwick OA, Oleksyn J, Zytkowiak R, Reich PB
773
(2007) Tree species effects on soil organic matter dynamics: The role of soil cation
774
composition. Ecosystems 10:999-1018. doi: 10.1007/s10021-007-9073-4
775 776 777
Intergovernmental Panel on Climate Change (2000) Land Use, Land-Use Change and Forestry - A Special Report of the IPCC. Cambridge University Press, Cambridge, UK IUSS Working Group (2015) World reference base for soil resources 2014 (update 2015),
778
international soil classification system for naming soils and creating legends for soil maps.
779
World Soil Resources Reports
780
Jandl R, Lindner M, Vesterdal L, Bauwens B, Baritz R, Hagedorn F, Johnson DW,
781
Minkkinen K, Byrne KA (2007) How strongly can forest management influence soil carbon
782
sequestration?. Geoderma 137:253-268. doi: 10.1016/j.geoderma.2006.09.003
32
783
Jarvis PG, Ibrom A, Linder S (2005) 'Carbon forestry': managing forests to conserve
784
carbon. In: Griffiths HG, Jarvis PG (eds) The Carbon Balance of Forest Biomes. Taylor &
785
Francis, pp 356-377
786
Jenkinson DS, Poulton PR, Bryant C (2008) The turnover of organic carbon in subsoils.
787
Part 1. Natural and bomb radiocarbon in soil profiles from the Rothamsted long-term field
788
experiments. Eur J Soil Sci 59:391-399. doi: 10.1111/j.1365-2389.2008.01025.x
789
Jobbágy EG, Jackson RB (2000) The vertical distribution of soil organic carbon and its
790
relation to climate and vegetation. Ecol Appl 10:423-436. doi: 10.1890/1051-
791
0761(2000)010[0423:TVDOSO]2.0.CO;2
792
Jonard M, Nicolas M, Coomes DA, Caignet I, Saenger A, Ponette Q (2017) Forest soils in
793
France are sequestering substantial amounts of carbon. Sci Total Environ 574:616-628. doi:
794
10.1016/j.scitotenv.2016.09.028
795 796 797 798 799
Kassambara A, Mundt F (2016) factoextra: Extract and Visualize the Results of Multivariate Data Analyses Kindermann G, McCallum I, Fritz S, Obersteiner M (2008) A global forest growing stock, biomass and carbon map based on FAO statistics. Silva Fenn 42. doi: 10.14214/sf.244 Lafargue E, Marquis F, Pillot D (1998) Rock-Eval 6 Applications in Hydrocarbon
800
Exploration, Production, and Soil Contamination Studies. Oil Gas Sci Technol 53:421-437.
801
doi: 10.2516/ogst:1998036
802
Laganière J, Paré D, Bergeron Y, Chen HYH (2012) The effect of boreal forest
803
composition on soil respiration is mediated through variations in soil temperature and C
804
quality. Soil Biol Biochem. doi: 10.1016/j.soilbio.2012.04.024
805
Leifeld J, Zimmerman M, Fuhrer J, Conen F (2009) Storage and turnover of carbon in
806
grassland soils along an elevation gradient in the Swiss Alps. Global Change Biol 15:668-
807
679. doi: 10.1111/j.1365-2486.2008.01782.x
33
808
Lorenz K, Lal R (2010) Carbon Sequestration in Forest Ecosystems. Springer Netherlands
809
Lorenz K, Lal R (2005) The depth distribution of soil organic carbon in relation to land use
810
and management and the potential of carbon sequestration in subsoil horizons. Adv Agron
811
88:35-66. doi: 10.1016/S0065-2113(05)88002-2
812
Mason JA, Jacobs PM, Gruley KE, Reyerson P, Hanson PR (2016) Parent material
813
influence on soil response to vegetation change, Southeastern Minnesota, U.S.A. Geoderma
814
275:1-17. doi: 10.1016/j.geoderma.2016.04.004
815
Mathieu JA, Hatté C, Balesdent J, Parent É (2015) Deep soil carbon dynamics are driven
816
more by soil type than by climate: a worldwide meta-analysis of radiocarbon profiles. Global
817
Change Biol 21:4278-4292. doi: 10.1111/gcb.13012
818
Meier IC, Leuschner C (2010) Variation of soil and biomass carbon pools in beech forests
819
across a precipitation gradient. Global Change Biol 16:1035-1045. doi: 10.1111/j.1365-
820
2486.2009.02074.x
821 822
Melillo JM, Aber JD, Muratore JF (1982) Nitrogen and lignin control of hardwood leaf litter decomposition dynamics. Ecology 63:621-626. doi: 10.2307/1936780
823
Moni C, Rumpel C, Virto I, Chabbi A, Chenu C (2010) Relative importance of sorption
824
versus aggregation for organic matter storage in subsoil horizons of two contrasting soils. Eur
825
J Soil Sci 61:958-969. doi: 10.1111/j.1365-2389.2010.01307.x
826
Mulder VL, Lacoste M, Martin MP, Richer-de-Forges A, Arrouays D (2015)
827
Understanding large-extent controls of soil organic carbon storage in relation to soil depth and
828
soil-landscape systems. Glob Biogeochem Cycles 29:1210-1229. doi:
829
10.1002/2015GB005178
830 831
Nabuurs GJ, Thürig E, Heidema N, Armolaitis K, Biber P, Cienciala E, Kaufmann E, Mäkipää R, Nilsen P, Petritsch R, Pristova T, Rock J, Schelhaas MJ, Sievanen R, Somogyi Z,
34
832
Vallet P (2008) Hotspots of the European forests carbon cycle. For Ecol Manag. doi:
833
10.1016/j.foreco.2008.04.009
834
Norby RJ, Cotrufo MF, Ineson P, O’Neill EG, Canadell JG (2001) Elevated CO2, litter
835
chemistry, and decomposition: a synthesis. Oecologia 127:153-165. doi:
836
10.1007/s004420000615
837
Olsen HR, Van Miegroet H (2010) Factors affecting carbon dioxide release from forest and
838
rangeland soils in Northern Utah. Soil Sci Soc Am J 74:282-291. doi: 10.2136/sssaj2009.0095
839
Pan Y, Birdsey RA, Fang J, Houghton RA, Kauppi PE, Kurz WA, Phillips OL, Shvidenko
840
AZ, Lewis SL, Canadell JG, Ciais P, Jackson RB, Pacala SW, McGuire AD, Piao S,
841
Rautiainen A, Sitch S, Hayes D (2011) A large and persistent carbon sink in the world's
842
forests. Science 333:988-993. doi: 10.1126/science.1201609
843 844 845 846 847 848
Pinheiro J, Bates D, DebRoy S, Sarkar D, Team RC (2016) nlme: Linear and Nonlinear Mixed Effects Models Plante AF, Fernández JM, Leifeld J (2009) Application of thermal analysis techniques in soil science. Geoderma 153:1-10. doi: 10.1016/j.geoderma.2009.08.016 Poeplau C, Don A (2013) Sensitivity of soil organic carbon stocks and fractions to different land-use changes across Europe. Geoderma. doi: 10.1016/j.geoderma.2012.08.003
849
Ponette Q, Ulrich E, Brêthes A, Bonneau M, Lanier M (1997) RENECOFOR - Chimie des
850
sols dans les 102 peuplements du réseau : campagne de mesures 1993-95. ONF, Département
851
des recherches techniques, Fontainebleau, France
852
Prescott CE (2010) Litter decomposition: what controls it and how can we alter it to
853
sequester more carbon in forest soils? Biogeochemistry 101:133-149. doi: 10.1007/s10533-
854
010-9439-0
35
855
Quideau SA, Chadwick OA, Benesi A, Graham RC, Anderson MA (2001) A direct link
856
between forest vegetation type and soil organic matter composition. Geoderma. doi:
857
10.1016/S0016-7061(01)00055-6
858
R Core Team (2016) R: A Language and Environment for Statistical Computing
859
Rowley MC, Grand S, Verrecchia EP (2018) Calcium-mediated stabilisation of soil
860 861
organic carbon. Biogeochemistry 137:27-49. doi: 10.1007/s10533-017-0410-1 Rumpel C, Kögel-Knabner I (2010) Deep soil organic matter---a key but poorly
862
understood component of terrestrial C cycle. Plant Soil 338:143-158. doi: 10.1007/s11104-
863
010-0391-5
864
Saenger A, Cécillon L, Poulenard J, Bureau F, De Daniéli S, Gonzalez J, Brun J (2015)
865
Surveying the carbon pools of mountain soils: A comparison of physical fractionation and
866
Rock-Eval pyrolysis. Geoderma 241–242:279-288. doi: 10.1016/j.geoderma.2014.12.001
867
Salomé C, Nunan N, Pouteau V, Lerch TZ, Chenu C (2010) Carbon dynamics in topsoil
868
and in subsoil may be controlled by different regulatory mechanisms. Global Change Biol
869
16:416-426. doi: 10.1111/j.1365-2486.2009.01884.x
870
Schiedung M, Don A, Wordell-Dietrich P, Alcántara V, Kuner P, Guggenberger G (2017)
871
Thermal oxidation does not fractionate soil organic carbon with differing biological
872
stabilities. J Plant Nutr Soil Sci 180:18-26. doi: 10.1002/jpln.201600172
873
Schmatz R, Recous S, Aita C, Tahir MM, Schu AL, Chaves B, Giacomini SJ (2017) Crop
874
residue quality and soil type influence the priming effect but not the fate of crop residue C.
875
Plant Soil 414:229-245. doi: 10.1007/s11104-016-3120-x
876
Schmidt MWI, Torn MS, Abiven S, Dittmar T, Guggenberger G, Janssens IA, Kleber M,
877
Kögel-Knabner I, Lehmann J, Manning DAC, Nannipieri P, Rasse DP, Weiner S, Trumbore
878
SE (2011) Persistence of soil organic matter as an ecosystem property. Nature 478:49-56. doi:
879
10.1038/nature10386
36
880
Schrumpf M, Kaiser K, Guggenberger G, Persson T, Kögel-Knabner I, Schulze E- (2013)
881
Storage and stability of organic carbon in soils as related to depth, occlusion within
882
aggregates, and attachment to minerals. Biogeosciences 10:1675-1691. doi: 10.5194/bg-10-
883
1675-2013
884
Schrumpf M, Kaiser K (2015) Large differences in estimates of soil organic carbon
885
turnover in density fractions by using single and repeated radiocarbon inventories. Geoderma
886
239–240:168-178. doi: 10.1016/j.geoderma.2014.09.025
887
Sebag D, Verrecchia EP, Cécillon L, Adatte T, Albrecht R, Aubert M, Bureau F, Cailleau
888
G, Copard Y, Decaens T, Disnar J-, Hetényi M, Nyilas T, Trombino L (2016) Dynamics of
889
soil organic matter based on new Rock-Eval indices. Geoderma 284:185-203. doi:
890
10.1016/j.geoderma.2016.08.025
891
Six J, Conant RT, Paul EA, Paustian K (2002) Stabilization mechanisms of soil organic
892
matter: Implications for C-saturation of soils. Plant Soil 241:155-176. doi:
893
10.1023/A:1016125726789
894
Sjögersten S, Alewell C, Cécillon L, Hagedorn F, Jandl R, Leifeld J, Martinsen V,
895
Schindlbacher A, Sebastià M-, Van Miegroet H (2011) Mountain Soils in a Changing
896
Climate? Vulnerability of Carbon Stocks and Ecosystem Feedbacks. In: Soil Carbon in
897
Sensitive European Ecosystems. John Wiley & Sons, Ltd, Chichester, UK, pp 118-148
898
Smith P, Bustamante M, Ahammad H, Clark H, Dong H, Elsiddig EA, Haberl H, Harper
899
R, House J, Jafari M, Masera O, Mbow C, Ravindranath NH, Rice CW, Robledo Abad C,
900
Romanovskaya A, Sperling F, Tubiello F (2014) Agriculture, Forestry and Other Land Use
901
(AFOLU). In: Edenhofer O, Pichs-Madruga R, Sokona Y, Farahani E, Kadner S, Seyboth K,
902
Adler A, Baum I, Brunner S, Eickemeier P, Kriemann B, Savolainen J, Schlömer S, von
903
Stechow C, Zwickel T, Minx JC (eds) Climate Change 2014: Mitigation of Climate Change.
904
Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental
37
905
Panel on Climate Change. Cambridge University Press, Cambridge, UK and New York, NY,
906
USA, pp 811-922
907
Soucémarianadin LN, Cécillon L, Chenu C, Baudin F, Nicolas M, Girardin C, Barré P
908
(2018) Is Rock-Eval 6 thermal analysis a good indicator of soil organic carbon lability? - A
909
method-comparison study in forest soils. Soil Biol Biochem 117:108-116. doi:
910
10.1016/j.soilbio.2017.10.025
911
Tewksbury CE, Van Miegroet H (2007) Soil organic carbon dynamics along a climatic
912
gradient in a southern Appalachian spruce–fir forest. Can J For Res 37:1161-1172. doi:
913
10.1139/X06-317
914
Tian Q, He H, Cheng W, Bai Z, Wang Y, Zhang X (2016) Factors controlling soil organic
915
carbon stability along a temperate forest altitudinal gradient. Sci Rep 6:18783. doi:
916
10.1038/srep18783
917
Torn MS, Trumbore SE, Chadwick OA, Vitousek PM, Hendricks DM (1997) Mineral
918
control of soil organic carbon storage and turnover. Nature 389:170-173. doi: 10.1038/38260
919
Trumbore SE (1997) Potential responses of soil organic carbon to global environmental
920 921
change. Proc Natl Acad Sci U S A 94:8284-8291 Trumbore SE, Chadwick OA, Amundson R (1996) Rapid exchange between soil carbon
922
and atmospheric carbon dioxide driven by temperature change. Science. doi:
923
10.1126/science.272.5260.393
924
Tyrrell ML, Ross J, Kelty M (2012) Carbon Dynamics in the Temperate Forest. In: Ashton
925
MS, Tyrrell ML, Spalding D, Gentry B (eds) Managing Forest Carbon in a Changing Climate.
926
Springer Netherlands, Dordrecht, pp 77-107
927 928
Ulrich E (1995) Le réseau RENECOFOR : objectifs et réalisation. Rev for fr 47:107-124. doi: 10.4267/2042/26634
38
929
Van Miegroet H, Boettinger JL, Baker MA, Nielsen J, Evans D, Stum A (2005) Soil
930
carbon distribution and quality in a montane rangeland-forest mosaic in northern Utah. Forest
931
Ecol Manag. doi: 10.1016/j.foreco.2005.08.017
932
Vesterdal L, Elberling B, Christiansen JR, Callesen I, Schmidt IK (2012) Soil respiration
933
and rates of soil carbon turnover differ among six common European tree species. For Ecol
934
Manag 264:185-196. doi: 10.1016/j.foreco.2011.10.009
935
von Lützow M, Kögel-Knabner I, Ekschmitt K, Flessa H, Guggenberger G, Matzner E,
936
Marschner B (2007) SOM fractionation methods: Relevance to functional pools and to
937
stabilization mechanisms. Soil Biol Biochem 39:2183-2207. doi:
938
10.1016/j.soilbio.2007.03.007
939
Vos C, Jaconi A, Jacobs A, Don A (2017) Hot regions of labile and stable soil organic
940
carbon in Germany -- Spatial variability and driving factors. SOIL 2017:1-35. doi:
941
10.5194/soil-2017-30
942
Wander M (2004) Soil organic matter fractions and their relevance to soil function. In:
943
Magdoff F, Weil RR (eds) Soil Organic Matter in Sustainable Agriculture. CRC Press, pp 67-
944
102
945
Wang Q, Zhong M (2016) Composition and mineralization of soil organic carbon pools in
946
four single-tree species forest soils. Journal of Forestry Research 27:1277-1285. doi:
947
10.1007/s11676-016-0244-z
948
Wiesmeier M, Prietzel J, Barthold F, Spörlein P, Geuß U, Hangen E, Reischl A, Schilling
949
B, von Lützow M, Kögel-Knabner I (2013) Storage and drivers of organic carbon in forest
950
soils of southeast Germany (Bavaria) – Implications for carbon sequestration. For Ecol Manag
951
295:162-172. doi: 10.1016/j.foreco.2013.01.025
952
Wiesmeier M, Schad P, von Lützow M, Poeplau C, Spörlein P, Geuß U, Hangen E, Reischl
953
A, Schilling B, Kögel-Knabner I (2014) Quantification of functional soil organic carbon pools
39
954
for major soil units and land uses in southeast Germany (Bavaria). Agric Ecosyst Environ.
955
doi: 10.1016/j.agee.2013.12.028
956
Wordell-Dietrich P, Don A, Helfrich M (2017) Controlling factors for the stability of
957
subsoil carbon in a Dystric Cambisol. Geoderma. doi: 10.1016/j.geoderma.2016.08.023
958
You Y, Wang J, Sun X, Tang Z, Zhou Z, Sun OJ (2016) Differential controls on soil
959
carbon density and mineralization among contrasting forest types in a temperate forest
960
ecosystem. Sci Rep 6:22411. doi: 10.1038/srep22411
961 962 963
Zhang J, Song C, Wenyan Y (2007) Tillage effects on soil carbon fractions in the Sanjiang Plain, Northeast China. Soil Tillage Res. doi: 10.1016/j.still.2006.03.014 Zimmermann M, Leifeld J, Schmidt MWI, Smith P, Fuhrer J (2007) Measured soil organic
964
matter fractions can be related to pools in the RothC model. Eur J Soil Sci 58:658-667. doi:
965
10.1111/j.1365-2389.2006.00855.x
966
40
967
Figures captions
968
Fig. 1 (a) Location of the 53 study sites from the French national network for the long term
969
monitoring of forest ecosystems (RENECOFOR) and their repartition among the climatic
970
zones and vegetation types and soil classes; (b) Number of samples by depths and analyses
971
performed to assess SOC stability
972 973
Fig. 2 Evolution of respired-C, POM-C, T50_HC_PYR and T50_CO2_OX in the five soil layers of the
974
53 RENECOFOR plots. The horizontal black lines show the medians. The bottom and top of
975
the box show the first and third quartiles, respectively. n= 53 for layers 0–10 cm, 10–20 cm
976
and 20–40 cm; n = 50 (RE6 and POM-C) or 46 (respired-C) for layer 40–80 cm; n = 33 (RE6)
977
or 31 (respired-C) for layer 80–100 cm. For each indicator, different letters indicate
978
significant differences between the means of the different layers
979 980
Fig. 3 Variations in the indicators of SOC stability respired-C and POM-C (top) and thermal
981
indicators (T50_HC_PYR and T50_CO2_OX; bottom) as a function of vegetation type in the surface
982
(0–10 cm) layer. The horizontal black line shows the median for each vegetation type. The
983
bottom and top of the box show the first and third quartiles, respectively. n = 29 and 24 for
984
coniferous and deciduous plots respectively. For each indicator, different letters indicate
985
significant differences between the means of the different layers
986 987
Fig. 4 Variations in indicators of SOC stability respired-C and POM-C (top) and thermal
988
indicators (T50_HC_PYR and T50_CO2_OX; bottom) as a function of depth for all three soil classes.
989
n= 53 for layers 0–10 cm, 10–20 cm and 20–40 cm; n = 50 (RE6 and POM-C) or 46
990
(respired-C) for layer 40–80 cm; n = 33 (RE6) or 31 (respired-C) for layer 80–100 cm
991
41
992
Fig. 5 Variations in the thermal indicator T50_CO2_OX (stable SOC pool) in the three climatic
993
zones as a function of depth
994 995
Fig. 6 Principal components analysis (PCA) loadings plots (top) and biplots (bottom) of the 4
996
indicators of SOC stability (red arrows) along the first two principal component axes (PC1
997
and PC2) for two layers: (left) 0–10 cm (n = 53) and (right) 40–80 cm (n = 46). In the loading
998
plots, the physico-chemical parameters and climatic data (black arrows) were projected in the
999
circle of correlations for information. In the biplots, the samples were represented by their soil
1000
class and the 95% ellipses for the three soil classes were added
42
table 1
Click here to download table Table 1.docx
Table 1 Mean (and standard deviation) of SOC content, C/N ratio of the bulk soil and RE6-derived bulk chemistry parameters (HI, OIRE6), as well as the averaged values derived from Ponette et al. (1997) and Jonard et al. (2017) for the texture, pHwater and the cationic exchange capacity, in the five soil layers for the 53 RENECOFOR plots HI OIRE6 mg HC / mg O2 / g % g SOC) SOC (composite sample, determined in this study) 5.1 (2.7) 16.9 (4.5) 276 (77) 225 (37) 2.9 (2.0) 16.4 (4.9) 218 (72) 255 (46) 1.8 (1.4) 14.8 (4.3) 170 (57) 299 (68) 0.8 (0.8) 11.6 (3.8) 133 (33) 437 (137) 0.6 (0.5) 9.7 (4.0) 122 (27) 525 (145) SOC
depth (cm) 0–10 10–20 20–40 40–80 80–100 a
n 53 53 53 50 33
C/N ratio
clay
silt
sand
%
%
%
pHwater
CECa cmol+/kg
(averaged from Ponette et al., 1997 and Jonard et al. 2017) 23 (14) 36 (18) 42 (29) 4.9 (1.0) 13.3 (13.1) 21 (13) 37 (18) 42 (29) 5.1 (1.1) 10.9 (12.7) 20 (14) 36 (18) 43 (28) 5.4 (1.3) 10.0 (12.5) 20 (15) 32 (17) 48 (27) 5.8 (1.4) 7.3 (8.5) 22 (17) 34 (16) 44 (27) 6.1 (1.6) 7.5 (8.3)
determined by extraction of the exchangeable cations with barium chloride (ISO 11260:1994)
table 2
Click here to download table Table 2.docx
Table 2 Results (p-value) of the 3-way ANOVA (analysis by layer) with the factors soil class (soil), vegetation type (veg) and climatic zone (clim) and their interactions for each response variable obtained from respiration test (respired-C), POM fractionation (POM-C) and RE6 thermal analysis (T50_HC_PYR; T50_CO2_OX). If the response variable needed to be transformed to conform to the assumptions of ANOVA, the transformation that was used is specified. Significance is indicated as follows: ***: p < 0.001; **: p < 0.01; *: p < 0.05; .: p < 0.1; NS = not significant Factor
Interaction
Respiration test depth (cm) variable 0–10 respired-C 10–20 log10 (respired-C + 1) 20–40 log10 (respired-C + 1) 40–80 log10 (respired-C + 1) 80–100 log10 (respired-C + 1)
soil ** *** ** NS *
veg NS NS NS NS NS
clim NS NS NS NS NS
soil × veg NS NS NS NS NS
soil × clim NS NS NS NS NS
veg × clim NS NS NS NS NS
POM fractionation depth (cm) variable 0–10 POM-C 40–80 POM-C
soil * *
veg NS NS
clim NS NS
soil × veg NS NS
soil × clim NS NS
veg × clim NS NS
soil *** NS * NS NS ** NS NS * NS
veg *** NS NS NS NS *** * * NS NS
clim NS NS NS NS NS ** ** ** *** *
soil × veg NS . NS NS NS NS NS NS NS NS
soil × clim . NS NS NS NS NS * * NS NS
veg × clim NS NS NS NS NS NS NS NS NS NS
Rock-Eval thermal analysis depth (cm) 0–10 10–20 20–40 40–80 80–100 0–10 10–20 20–40 40–80 80–100
variable T50_HC_PYR T50_HC_PYR T50_HC_PYR T50_HC_PYR T50_HC_PYR T50_CO2_OX T50_CO2_OX T50_CO2_OX T50_CO2_OX T50_CO2_OX
table 3
Click here to download table Table 3.docx
Table 3 Spearman correlation coefficients between the RE6-derived temperature parameters (T50_HC_PYR and T50_CO2_OX), the 10-week mineralizable SOC (respired-C), the proportion of SOC in the POM fraction (POM-C) and the physico-chemical properties of the samples (C content; C/N ratio; HI; OIRE6; texture, clay and sand content; pH; cationic exchange capacity, CEC) and climatic data of the plots (mean annual precipitation, MAP; mean annual temperature, MAT). Significance is indicated as follows: ***: p < 0.001; **: p < 0.01; *: p < 0.05. The high (> 0.6) correlations are marked in bold. n = 242 for the RE6 parameters, n = 236 for respired-C and n = 103 for POM-C
SOC C/N ratio HI OIRE6 Clay Sand pHwater CEC MAP MAT
T50_HC_PYR −0.58*** −0.34*** −0.67*** 0.63*** −0.06 0.02 0.31*** −0.33*** 0.06 0.01
T50_CO2_OX −0.72*** −0.43*** −0.53*** 0.50*** −0.03 0.10 0.33*** −0.25*** −0.20** 0.19**
respired-C 0.03 −0.13* 0.08 −0.04 0.19** −0.15* 0.23*** 0.31*** −0.16* −0.09
POM-C 0.52*** 0.61*** 0.67*** −0.76*** −0.18 0.18 −0.54*** 0.08 −0.11 −0.02
Fig. 1
Click here to download colour figure Fig1.tif
Fig. 2
Click here to download colour figure Fig2.tif
Fig. 3
Click here to download colour figure Fig3.tif
Fig. 4
Click here to download colour figure Fig4.tif
Fig. 5
Click here to download colour figure Fig5.tif
Fig. 6
Click here to download colour figure Fig6.tif
Supplementary material captions
Online Resource 1 Description of the Rock-Eval 6 thermal analysis (adapted from Baudin et al., 2017) and calculation of the two RE6-derived parameters (hydrogen index; T50_CO2_OX, the temperature at which 50% of the residual SOM was oxidized to CO2 during the oxidation phase) Baudin F, Tribovillard N, Trichet J (2017) Géologie De La Matière Organique. EDP Sciences, Lilles, France.
Online Resource 2 Correlation between C content (%) of isovolumetrically pooled samples (measured in this study as detailed in Materials and Methods subsection a) and average values of the 5 replicates × 5 subplots from RENECOFOR samples (calculated with values from Jonard et al. (2017) and Ponette et al. (1997) for samples 0–40cm and 40–100 cm, respectively) for a given soil layer (n = 242). The 1:1 line has been added in red for reference Jonard M, Nicolas M, Coomes DA, Caignet I, Saenger A, Ponette Q (2017) Forest soils in France are sequestering substantial amounts of carbon. Sci Total Environ 574:616-628 Ponette Q, Ulrich E, Brêthes A, Bonneau M, Lanier M (1997) RENECOFOR - Chimie des sols dans les 102 peuplements du réseau : campagne de mesures 1993-95. ONF, Département des recherches techniques, Fontainebleau, France
Online Resource 3 Details of models and their significant terms selected to explain variations in respired-C and POM-C, T50_HC_PYR, and T50_CO2_OX in the 53 study plots (analysis by profile). All models used a gls function (see details in the Calculations and statistical analyses section)
Online Resource 4 Mean (and standard deviation) of the indicators of labile SOC (T50_HC_PYR, POM-C; respired-C) and stable SOC (T50_CO2_OX) for each soil class in the five different layers. The total SOC content was added for reference
Online Resource 5 Table of correlations for all samples and for each layer individually between the indicators of the SOC pools and the physico-chemical properties (SOC content, C/N ratio, HI, OIRE6, texture, pH, cationic exchange capacity), the climatic data of the plots (mean annual precipitation; MAP and mean annual temperature; MAT) and the chemical properties (C/N ratio) of the inputs and humus. Significance is indicated as follows: ***: p < 0.001; **: p < 0.01; *: p < 0.05. The high (> 0.6) correlations obtained with the SOC pools indicators are marked in bold. n = 242 total; n = 53 for layers 1 to 3 and n = 50 and n = 33 for layers 4 and 5 respectively unless specified otherwise
Online Resource 6 Distribution of the mean annual precipitation (MAP) and mean annual temperature (MAT) in the 53 study sites as a function of vegetation type illustrating a bias towards coniferous stands being in wetter and colder locations. n = 29 and 24 for coniferous and deciduous, respectively
Online Resource 3 Details of models and their significant terms selected to explain variations in respired-C and POM-C, T50_HC_PYR, and T50_CO2_OX in the 53 study plots (analysis by profile). All models used a gls function (see details in the Calculations and statistical analyses section) Response variable respired-C POM-C
Predictors in final model$ soil + depth + soil × log10(respired-C + 1) depth log10(POM-C) depth + soil Transformation
Level of significance p -value soil < 0.0001; depth =0.0823; depth × soil = 0.0417 depth < 0.0001 and soil = 0.0114
T50_HC_PYR
depth + soil + veg + depth and depth × soil < 0.0001; soil = 0.1440; veg depth × soil + depth = 0.0665; depth × veg = 0.0023; soil × veg = × veg + soil × veg 0.0236
T50_CO2_OX
depth + veg + climate + depth × climate
$
all < 0.0001 except climate = 0.0272
For all models, as a preliminary inspection of the variance of a given factor showed heterogeneity among the various layers, we used a constant variance function [varIdent(form= ~ 1 | depth)], which allowed five different variances, one for each soil layer. We also used the compound symmetry structure [corCompSymm(form =~ 1|plot)], which is similar to the variance structure of a random-intercept-only model. In our case, it allowed to treat each site as random factor.
Online Resource 4 Mean (and standard deviation) of the indicators of labile (T50_HC_PYR, POM-C), very labile (respired-C) and stable SOC (T50_CO2_OX) for each soil class in the five different layers. The basic soil physico-chemical properties (texture, total SOC content, C/N ratio and pH) were added for reference layer 0–10 cm
soil class
ALL dystric Cambisol eutric Cambisol entic Podzol 10–20 cm ALL dystric Cambisol eutric Cambisol entic Podzol 20–40 cm ALL dystric Cambisol eutric Cambisol entic Podzol 40–80 cm ALL dystric Cambisol eutric Cambisol entic Podzol 80–100 cm ALL dystric Cambisol eutric Cambisol entic Podzol 0–100 cm ALL
n 53 18 16 19 53 18 16 19 53 18 16 19 50 18 14 18 33 11 10 12 242
T50_HC_PYR (°C) T50_CO2_OX (°C) 422 420 428 417 434 434 435 433 441 444 437 443 448 449 444 450 452 450 448 458 439
(8) (8) (7) (9) (7) (8) (6) (8) (7) (8) (5) (6) (10) (7) (9) (12) (13) (11) (11) (18) (9)
399 399 403 396 408 409 410 404 418 421 417 417 431 438 424 430 437 445 427 439 419
(8) (8) (10) (8) (12) (10) (14) (11) (15) (10) (16) (17) (17) (15) (19) (18) (17) (12) (16) (22) (14)
POM-C (% OC)
n 53 18 16 19
50 18 14 18
103
22.6 23.0 19.2 25.1 nd nd nd nd nd nd nd nd 11.5 13.3 7.8 12.5 nd nd nd nd 17.2
(7.3) (7.5) (5.6) (7.6)
(6.2) (6.3) (3.5) (6.8)
(8.8)
n 53 18 16 19 53 18 16 19 53 18 16 19 46 17 14 15 31 11 10 10 236
respired-C (µg −1 CO2-C·µg soil C in %) 1.73 1.87 2.19 1.13 1.61 1.54 2.13 1.15 1.43 1.38 1.86 1.06 1.34 1.48 1.51 1.03 1.17 1.57 1.14 0.79 1.46
(0.57) (0.70) (0.62) (0.38) (0.56) (0.64) (0.74) (0.31) (0.68) (0.61) (0.84) (0.58) (0.67) (0.62) (0.75) (0.64) (0.66) (0.91) (0.59) (0.49) (0.63)
n 53 18 16 19 53 18 16 19 53 18 16 19 50 18 14 18 33 11 10 12 242
clay (%) 22 18 37 15 21 17 36 13 20 17 35 11 20 18 35 11 22 20 38 10 21
(14) (9) (11) (9) (13) (9) (11) (7) (14) (9) (13) (6) (15) (10) (18) (5) (17) (11) (19) (4) (14)
silt (%) 36 31 50 28 37 32 51 29 36 33 48 30 32 28 41 28 34 30 41 30 35
(18) (14) (13) (19) (18) (14) (11) (20) (18) (14) (12) (20) (17) (11) (13) (20) (16) (12) (12) (21) (17)
sand (%) 42 51 13 57 42 51 13 58 43 51 17 59 48 54 24 62 44 50 21 59 44
(29) (21) (20) (25) (29) (21) (17) (25) (28) (21) (17) (25) (27) (19) (25) (25) (27) (19) (23) (23) (28)
C content (%) 5.1 4.1 5.1 6 2.9 1.9 3.5 3.3 1.8 1.1 2.4 2.1 0.8 0.5 0.9 1.1 0.6 0.3 0.7 0.8 2.4
(2.7) (1.8) (1.9) (3.6) (2.0) (0.7) (1.6) (2.7) (1.4) (0.4) (1.4) (1.8) (0.8) (0.3) (0.5) (1.1) (0.5) (0.2) (0.2) (0.7) (2.4)
C/N ratio 16.9 16.8 13.3 19.9 16.4 16.3 12.4 19.8 14.8 14.8 11.3 17.7 11.6 10.5 9.3 14.6 9.7 7.2 8.4 13.1 14.2
(4.5) (3.4) (1.5) (5.0) (4.9) (3.5) (1.1) (5.4) (4.3) (3.8) (1.3) (4.3) (3.8) (3.5) (1.9) (3.4) (4.0) (1.9) (1.6) (4.6) (5.0)
pH 4.9 4.5 6.2 4.1 5.1 4.6 6.5 4.3 5.4 4.8 7.2 4.6 5.8 5.1 7.9 4.8 6.1 5.3 8.2 5.0 5.4
(1.0) (0.3) (0.9) (0.2) (1.1) (0.3) (1.0) (0.2) (1.3) (0.3) (1.0) (0.1) (1.4) (0.6) (0.6) (0.2) (1.6) (1.0) (0.6) (0.4) (1.3)
Online Resource 5 Table of correlations for all samples and for each layer individually between the indicators of the SOC pools and the physico-chemical properties (SOC content, C/N ratio, HI, OIRE6, texture, pH, cationic exchange capacity), the climatic data of the plots (mean annual precipitation; MAP and mean annual temperature; MAT) and the chemical properties (C/N ratio) of the inputs and humus. Significance is indicated as follows: ***: p < 0.001; **: p < 0.01; *: p < 0.05. The high (> 0.6) correlations obtained with the SOC pools indicators are marked in bold. n = 242 total; n = 53 for layers 1 to 3 and n = 50 and n = 33 for layers 4 and 5 respectively unless specified otherwise OIRE6 Clay pHwater C/N ratio (soil) HI Sand CEC MAP MAT C/N ratio (inputs) POM-C§ respired-C* T50_HC_PYR T50_CO2_OX SOC respired-C
0.20
T50_HC_PYR
−0.73***
−0.32***
T50_CO2_OX SOC C/N ratio (soil) HI OIRE6 Clay Sand pHwater CEC MAP MAT C/N ratio (inputs) C/N ratio (humus)
−0.56***
−0.15* 0.03 −0.13* 0.08
0.67*** −0.58*** −0.34*** −0.67***
−0.72*** −0.43*** −0.53***
0.42*** 0.58***
0.62***
−0.18 0.18
−0.04 0.19** −0.15*
0.63*** −0.06 0.02
0.50*** −0.03 0.10
−0.56*** 0.31*** −0.32***
−0.78*** −0.51*** 0.45***
−0.84*** −0.16* 0.31*** 0.14* −0.28***
−0.89***
−0.54*** 0.08 −0.11 −0.02 0.22* 0.01
0.23*** 0.31*** −0.16* −0.09 0.16* 0.38***
0.31*** −0.33*** 0.06 0.01 −0.09 −0.14*
0.33*** −0.25*** −0.20** 0.19** −0.16* −0.11
−0.22*** 0.48*** 0.36*** −0.25*** −0.07 −0.01
−0.61*** −0.31*** −0.01 −0.02 0.24*** −0.03
−0.45*** 0.59*** 0.14* 0.06 −0.06 0.12 0.13* −0.11 −0.05 −0.06 −0.08 0.10
0.44*** 0.74*** 0.14* −0.08 −0.24*** 0.12
−0.45*** −0.69*** −0.17** 0.06 0.23*** −0.14*
0.47*** 0.07 0.12 −0.22*** −0.22*** −0.60*** −0.03 −0.17** −0.24*** 0.40*** 0.18** −0.05
−0.08 −0.06
0.44***
§
*
Clay
Sand
pHwater
CEC
MAT
C/N ratio (inputs)
−0.62*** −0.72*** −0.21 0.13 0.28* −0.16
0.64*** 0.06 −0.21 −0.14 0.42**
0.39** −0.45*** −0.61*** −0.16 −0.25 0.14 −0.05
−0.06 −0.07
0.45***
0.52*** 0.61*** 0.67*** −0.76***
n = 99 LAYER 1: 0–10 cm POM-C
n = 236
respired-C
T50_HC_PYR
T50_CO2_OX SOC
C/N ratio (soil)
HI
OIRE6
respired-C
−0.29*
T50_HC_PYR
−0.44***
T50_CO2_OX SOC C/N ratio (soil) HI OIRE6 Clay Sand pHwater CEC MAP MAT C/N ratio (inputs) C/N ratio (humus)
−0.19 0.17 0.56*** 0.32*
0.13 −0.20 −0.51*** −0.43**
0.45*** −0.10 −0.55*** −0.30*
−0.28* −0.19 0.07
−0.08 −0.29*
0.67***
−0.41** −0.29* 0.32*
0.52*** 0.43** −0.41**
0.40** 0.44** −0.50***
−0.06 −0.03 −0.08
0.25 0.44** −0.32*
−0.80*** −0.70*** 0.65***
−0.91*** −0.71*** 0.75*** 0.61*** −0.65***
−0.88***
−0.35** −0.16 −0.10 −0.09 0.31* −0.04
0.62*** 0.30* 0.10 −0.30* −0.03 0.47***
0.46*** 0.35* −0.11 0.14 −0.41** −0.10
0.36** 0.00 −0.29* 0.25 −0.25 0.10
0.06 0.60*** 0.53*** −0.39** −0.07 −0.17
−0.70*** −0.60*** −0.25 0.10 0.47*** −0.07
−0.61*** 0.70*** −0.72*** 0.72*** −0.37** 0.35* 0.32* −0.30* −0.02 −0.13 −0.41** 0.34*
0.60*** 0.78*** 0.33* −0.26 −0.26 0.11
MAP
0.12
LAYER 2: 10–20 cm POM-C
respired-C
T50_HC_PYR
T50_CO2_OX SOC
C/N ratio (soil)
HI
OIRE6
Clay
Sand
pHwater
CEC
MAP
MAT
C/N ratio (inputs)
respired-C
nd
T50_HC_PYR
nd
0.00
T50_CO2_OX SOC C/N ratio (soil) HI OIRE6 Clay Sand pHwater CEC MAP MAT C/N ratio (inputs) C/N ratio (humus)
nd nd nd nd
0.02 −0.13 −0.33* −0.24
0.40** 0.08 −0.13 −0.26
−0.09 −0.04 0.14
−0.34* −0.28*
nd nd nd
0.34* 0.29* −0.29*
0.21 0.10 −0.12
−0.22 0.07 −0.05
0.37** 0.60*** −0.52***
−0.73*** −0.71*** 0.66***
−0.74*** −0.53*** 0.65*** 0.44** −0.58***
−0.88***
nd nd nd nd nd nd
0.60*** 0.27* −0.08 −0.20 0.20 0.53***
0.08 0.13 −0.06 −0.05 −0.05 −0.12
0.29* −0.01 −0.24 0.21 −0.25 −0.01
0.28* 0.73*** 0.66*** −0.47*** −0.21 −0.02
−0.62*** −0.65*** −0.20 0.12 0.32* −0.14
−0.36** −0.40** −0.17 0.25 −0.12 −0.22
0.59*** 0.76*** 0.30* −0.20 −0.26 0.14
−0.58*** −0.77*** −0.24 0.12 0.28* −0.15
0.64*** 0.10 −0.27 −0.05 0.51***
0.39** −0.42** −0.23 0.11
−0.61*** −0.25 −0.05
−0.06 −0.07
0.45***
Clay
Sand
pHwater
CEC
MAP
MAT
C/N ratio (inputs)
−0.50*** −0.76*** −0.25 0.13 0.27 −0.11
0.65*** 0.07 −0.26 0.10 0.59***
0.31* −0.35** −0.27 0.14
−0.61*** −0.25 −0.05
−0.06 −0.07
0.45***
0.60***
0.51*** 0.60*** 0.23 −0.29* 0.02 0.27*
LAYER 3: 20–40 cm POM-C
respired-C
T50_HC_PYR
T50_CO2_OX SOC
C/N ratio (soil)
HI
OIRE6
respired-C
nd
T50_HC_PYR
nd
−0.33*
T50_CO2_OX SOC C/N ratio (soil) HI OIRE6 Clay Sand pHwater CEC MAP MAT C/N ratio (inputs) C/N ratio (humus)
nd nd nd nd
0.06 −0.21 −0.27 −0.10
0.37** −0.12 0.21 −0.21
−0.41** 0.06 0.05
−0.15 0.13
nd nd nd
0.11 0.29* −0.23
−0.05 −0.22 0.21
−0.31* −0.04 0.15
0.20 0.46*** −0.48***
−0.76*** −0.74*** 0.68***
−0.65*** −0.34* 0.70*** 0.24 −0.64***
−0.90***
nd nd nd nd nd nd
0.45*** 0.19 −0.15 0.00 0.04 0.41**
−0.32* −0.30* 0.02 −0.07 0.12 −0.26
−0.15 −0.24 −0.38** 0.33* −0.16 −0.19
0.29* 0.62*** 0.72*** −0.49*** −0.26 −0.02
−0.47*** −0.59*** −0.05 0.03 0.26 −0.09
−0.11 −0.13 0.10 0.16 −0.25 −0.17
0.54*** 0.77*** 0.20 −0.16 −0.25 0.13
0.48***
0.40** 0.51*** 0.11 −0.25 0.00 0.19
LAYER 4: 40–80 cm POM-C respired-C
respired-C*
T50_HC_PYR
T50_CO2_OX SOC
C/N ratio (soil)
HI
OIRE6
Clay
Sand
pHwater
CEC
MAP
MAT
C/N ratio (inputs)
0.47***
T50_HC_PYR
−0.35*
−0.41**
T50_CO2_OX SOC C/N ratio (soil) HI OIRE6 Clay Sand pHwater CEC MAP MAT C/N ratio (inputs) C/N ratio (humus)
−0.01 −0.43** 0.30* 0.06
−0.01 −0.54*** −0.17 −0.03
0.19 0.21 0.13 −0.21
−0.42** −0.34* −0.42**
0.13 −0.08 0.16
0.11 0.03 −0.12
0.46*** −0.34* −0.25 0.04 0.33* 0.10
0.03 0.13 −0.42** 0.09 0.30* 0.29
−0.02 −0.08 0.16 0.13 −0.19 −0.27
*
LAYER 5: 80–100 cm POM-C
−0.49*** −0.21 −0.24 0.22 0.12 0.10 −0.18 −0.02 −0.45** 0.50*** −0.22 −0.29*
0.23
−0.07 0.33* −0.48***
−0.65*** −0.55*** 0.44**
−0.43** −0.06 0.04
0.62*** −0.51***
−0.90***
0.20 0.25 0.69*** −0.48*** −0.15 0.02
−0.27 −0.49*** 0.36* −0.28* 0.21 0.13
0.09 0.05 0.17 0.03 −0.08 0.01
0.28 0.52*** −0.13 0.16 −0.12 0.11
0.36* 0.80*** −0.03 0.08 −0.31* 0.00
−0.36* −0.75*** −0.12 0.01 0.26 −0.06
0.66*** −0.04 −0.15 0.01 0.37**
−0.07 −0.06 −0.26 0.15
−0.58*** −0.24 −0.04
−0.11 −0.10
0.44**
Clay
Sand
pHwater
CEC
MAP
MAT
C/N ratio (inputs)
−0.37* −0.60*** 0.10 −0.17 −0.05 −0.34
0.64*** 0.04 −0.24 −0.03 0.14
−0.24 0.03 −0.06 0.25
−0.54** −0.12 −0.06
−0.13 0.05
0.41*
n = 46
respired-C*
T50_HC_PYR
T50_CO2_OX SOC
respired-C
nd
T50_HC_PYR
nd
−0.48**
T50_CO2_OX SOC C/N ratio (soil) HI OIRE6 Clay Sand pHwater CEC MAP MAT C/N ratio (inputs) C/N ratio (humus)
nd nd nd nd
−0.12 −0.43* −0.30 0.17
0.08 0.34 0.37* −0.39*
nd nd nd
0.31 −0.10 0.09
−0.18 −0.11 −0.06
0.25 0.02 0.17
nd nd nd nd nd nd
−0.01 0.27 −0.21 −0.12 0.45* 0.25
−0.03 −0.36* 0.26 0.09 −0.12 −0.07
−0.46** −0.13 −0.34 0.43* −0.20 −0.22
*
0.29* 0.19
n = 31
−0.69*** −0.26 −0.30
0.39* 0.04
C/N ratio (soil)
HI
OIRE6
0.00
−0.29 0.14 −0.30
−0.58*** −0.51** 0.41*
−0.14 0.09 −0.06
0.38* −0.29
−0.86***
0.39* 0.04 0.60*** −0.43* −0.01 0.13
−0.12 −0.36* 0.46** −0.27 −0.08 0.02
0.22 0.30 0.05 0.13 −0.21 0.04
−0.18 0.21 −0.11 0.29 0.13 0.14
0.36* 0.72*** −0.30 0.27 −0.09 0.27
